{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "q=open('F:/工程学院/大四/大数据分析综合实训/大数据分析综合实训-附件.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>蔬果</td>\n",
       "      <td>1201</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>120109</td>\n",
       "      <td>其它蔬菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1201090311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>生鲜</td>\n",
       "      <td>个</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称      销售日期    销售月份  \\\n",
       "0     0    12   蔬果  1201    蔬菜  120109    其它蔬菜  20150101  201501   \n",
       "1     1    20   粮油  2014   酱菜类  201401      榨菜  20150101  201501   \n",
       "2     2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "3     3    15   日配  1503  冷藏料理  150305   冷藏面食类  20150101  201501   \n",
       "4     4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "\n",
       "            商品编码    规格型号  商品类型 单位  销售数量  销售金额  商品单价 是否促销  \n",
       "0  DW-1201090311     NaN    生鲜  个   8.0   4.0   2.0    否  \n",
       "1  DW-2014010019     60g  一般商品  袋   6.0   3.0   0.5    否  \n",
       "2  DW-1505020011    150g  一般商品  袋   1.0   2.4   2.4    否  \n",
       "3  DW-1503050035    500g  一般商品  袋   1.0   6.5   8.3    否  \n",
       "4  DW-1505020020  100g*8  一般商品  袋   1.0  11.9  11.9    否  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(q)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        16.00000\n",
       "1         3.00000\n",
       "2         2.40000\n",
       "3         8.30000\n",
       "4        11.90000\n",
       "           ...   \n",
       "42811     3.90600\n",
       "42812     0.86240\n",
       "42813    14.50000\n",
       "42814     1.83808\n",
       "42815     8.00000\n",
       "Name: 销售金额, Length: 42816, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 新增销售金额字段\n",
    "data['销售金额'] = data['销售数量'].values * data['商品单价'].values\n",
    "data['销售金额']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>蔬果</td>\n",
       "      <td>1201</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>120109</td>\n",
       "      <td>其它蔬菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1201090311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>生鲜</td>\n",
       "      <td>个</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称      销售日期    销售月份  \\\n",
       "0     0    12   蔬果  1201    蔬菜  120109    其它蔬菜  20150101  201501   \n",
       "1     1    20   粮油  2014   酱菜类  201401      榨菜  20150101  201501   \n",
       "2     2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "3     3    15   日配  1503  冷藏料理  150305   冷藏面食类  20150101  201501   \n",
       "4     4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "\n",
       "            商品编码    规格型号  商品类型 单位  销售数量  销售金额  商品单价 是否促销  \n",
       "0  DW-1201090311     NaN    生鲜  个   8.0  16.0   2.0    否  \n",
       "1  DW-2014010019     60g  一般商品  袋   6.0   3.0   0.5    否  \n",
       "2  DW-1505020011    150g  一般商品  袋   1.0   2.4   2.4    否  \n",
       "3  DW-1503050035    500g  一般商品  袋   1.0   8.3   8.3    否  \n",
       "4  DW-1505020020  100g*8  一般商品  袋   1.0  11.9  11.9    否  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 42816 entries, 0 to 42815\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   顾客编号    42816 non-null  int64  \n",
      " 1   大类编码    42816 non-null  int64  \n",
      " 2   大类名称    42816 non-null  object \n",
      " 3   中类编码    42816 non-null  int64  \n",
      " 4   中类名称    42816 non-null  object \n",
      " 5   小类编码    42816 non-null  int64  \n",
      " 6   小类名称    42816 non-null  object \n",
      " 7   销售日期    42816 non-null  int64  \n",
      " 8   销售月份    42816 non-null  int64  \n",
      " 9   商品编码    42816 non-null  object \n",
      " 10  规格型号    42720 non-null  object \n",
      " 11  商品类型    42816 non-null  object \n",
      " 12  单位      42816 non-null  object \n",
      " 13  销售数量    42814 non-null  float64\n",
      " 14  销售金额    42814 non-null  float64\n",
      " 15  商品单价    42816 non-null  float64\n",
      " 16  是否促销    42816 non-null  object \n",
      "dtypes: float64(3), int64(6), object(8)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#2、缺失值处理\n",
    "data.info()#通过info()发现只有销售数量和销售金额有空值且空值数量很小，所以可以进行删除操作，其余数据都没有空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data=data.dropna()           #删除字段中有缺失值的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 42718 entries, 1 to 42815\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   顾客编号    42718 non-null  int64  \n",
      " 1   大类编码    42718 non-null  int64  \n",
      " 2   大类名称    42718 non-null  object \n",
      " 3   中类编码    42718 non-null  int64  \n",
      " 4   中类名称    42718 non-null  object \n",
      " 5   小类编码    42718 non-null  int64  \n",
      " 6   小类名称    42718 non-null  object \n",
      " 7   销售日期    42718 non-null  int64  \n",
      " 8   销售月份    42718 non-null  int64  \n",
      " 9   商品编码    42718 non-null  object \n",
      " 10  规格型号    42718 non-null  object \n",
      " 11  商品类型    42718 non-null  object \n",
      " 12  单位      42718 non-null  object \n",
      " 13  销售数量    42718 non-null  float64\n",
      " 14  销售金额    42718 non-null  float64\n",
      " 15  商品单价    42718 non-null  float64\n",
      " 16  是否促销    42718 non-null  object \n",
      "dtypes: float64(3), int64(6), object(8)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "new_data.info()\n",
    "# 统计是否有空值——缺失值处理完成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "顾客编号    0\n",
       "大类编码    0\n",
       "大类名称    0\n",
       "中类编码    0\n",
       "中类名称    0\n",
       "小类编码    0\n",
       "小类名称    0\n",
       "销售日期    0\n",
       "销售月份    0\n",
       "商品编码    0\n",
       "规格型号    0\n",
       "商品类型    0\n",
       "单位      0\n",
       "销售数量    0\n",
       "销售金额    0\n",
       "商品单价    0\n",
       "是否促销    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42718.000000</td>\n",
       "      <td>42718.000000</td>\n",
       "      <td>42718.000000</td>\n",
       "      <td>42718.000000</td>\n",
       "      <td>4.271800e+04</td>\n",
       "      <td>42718.000000</td>\n",
       "      <td>42718.000000</td>\n",
       "      <td>42718.000000</td>\n",
       "      <td>42718.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>877.951589</td>\n",
       "      <td>17.943677</td>\n",
       "      <td>1800.399831</td>\n",
       "      <td>180048.553373</td>\n",
       "      <td>2.015026e+07</td>\n",
       "      <td>201502.446627</td>\n",
       "      <td>1.196414</td>\n",
       "      <td>11.392446</td>\n",
       "      <td>12.554783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>695.364879</td>\n",
       "      <td>6.059244</td>\n",
       "      <td>608.736367</td>\n",
       "      <td>60876.251067</td>\n",
       "      <td>1.150736e+02</td>\n",
       "      <td>1.149049</td>\n",
       "      <td>2.518524</td>\n",
       "      <td>43.753429</td>\n",
       "      <td>16.669193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>100101.000000</td>\n",
       "      <td>2.015010e+07</td>\n",
       "      <td>201501.000000</td>\n",
       "      <td>-16.000000</td>\n",
       "      <td>-165.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>280.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>120106.000000</td>\n",
       "      <td>2.015013e+07</td>\n",
       "      <td>201501.000000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>2.992200</td>\n",
       "      <td>3.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>729.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>200105.000000</td>\n",
       "      <td>2.015022e+07</td>\n",
       "      <td>201502.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>6.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1352.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2203.000000</td>\n",
       "      <td>220306.000000</td>\n",
       "      <td>2.015040e+07</td>\n",
       "      <td>201504.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.088995</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2611.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>3436.000000</td>\n",
       "      <td>343699.000000</td>\n",
       "      <td>2.015043e+07</td>\n",
       "      <td>201504.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>5340.000000</td>\n",
       "      <td>890.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               顾客编号          大类编码          中类编码           小类编码          销售日期  \\\n",
       "count  42718.000000  42718.000000  42718.000000   42718.000000  4.271800e+04   \n",
       "mean     877.951589     17.943677   1800.399831  180048.553373  2.015026e+07   \n",
       "std      695.364879      6.059244    608.736367   60876.251067  1.150736e+02   \n",
       "min        0.000000     10.000000   1001.000000  100101.000000  2.015010e+07   \n",
       "25%      280.000000     12.000000   1201.000000  120106.000000  2.015013e+07   \n",
       "50%      729.000000     20.000000   2001.000000  200105.000000  2.015022e+07   \n",
       "75%     1352.000000     22.000000   2203.000000  220306.000000  2.015040e+07   \n",
       "max     2611.000000     34.000000   3436.000000  343699.000000  2.015043e+07   \n",
       "\n",
       "                销售月份          销售数量          销售金额          商品单价  \n",
       "count   42718.000000  42718.000000  42718.000000  42718.000000  \n",
       "mean   201502.446627      1.196414     11.392446     12.554783  \n",
       "std         1.149049      2.518524     43.753429     16.669193  \n",
       "min    201501.000000    -16.000000   -165.000000      0.000000  \n",
       "25%    201501.000000      0.530000      2.992200      3.960000  \n",
       "50%    201502.000000      1.000000      5.900000      6.900000  \n",
       "75%    201504.000000      1.000000     12.088995     15.000000  \n",
       "max    201504.000000    216.000000   5340.000000    890.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3、异常值处理\n",
    "new_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 销售数量异常值分析\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif']=['SimHei'] #绘图中增加中文\n",
    "df = pd.DataFrame(new_data['销售数量'])\n",
    "df.plot.box(title=\"销售数量异常值分析\")\n",
    "plt.grid(linestyle=\"--\", alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         True\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "5        False\n",
       "         ...  \n",
       "42811    False\n",
       "42812    False\n",
       "42813    False\n",
       "42814    False\n",
       "42815    False\n",
       "Name: 销售数量, Length: 42718, dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data['销售数量']>new_data['销售数量'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "异常值6\n",
      "异常值16\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值5\n",
      "异常值4\n",
      "异常值4\n",
      "异常值5\n",
      "异常值5\n",
      "异常值12\n",
      "异常值16\n",
      "异常值12\n",
      "异常值12\n",
      "异常值16\n",
      "异常值16\n",
      "异常值12\n",
      "异常值8\n",
      "异常值4\n",
      "异常值12\n",
      "异常值20\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值6\n",
      "异常值16\n",
      "异常值7\n",
      "异常值4\n",
      "异常值-2\n",
      "异常值4\n",
      "异常值4\n",
      "异常值5\n",
      "异常值16\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值16\n",
      "异常值16\n",
      "异常值6\n",
      "异常值4\n",
      "异常值6\n",
      "异常值5\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值24\n",
      "异常值10\n",
      "异常值12\n",
      "异常值3\n",
      "异常值12\n",
      "异常值4\n",
      "异常值6\n",
      "异常值9\n",
      "异常值5\n",
      "异常值4\n",
      "异常值4\n",
      "异常值10\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值16\n",
      "异常值12\n",
      "异常值4\n",
      "异常值5\n",
      "异常值16\n",
      "异常值4\n",
      "异常值5\n",
      "异常值13\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值6\n",
      "异常值24\n",
      "异常值4\n",
      "异常值6\n",
      "异常值20\n",
      "异常值6\n",
      "异常值5\n",
      "异常值12\n",
      "异常值24\n",
      "异常值4\n",
      "异常值3\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值24\n",
      "异常值12\n",
      "异常值12\n",
      "异常值9\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值24\n",
      "异常值8\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值5\n",
      "异常值12\n",
      "异常值16\n",
      "异常值24\n",
      "异常值86\n",
      "异常值16\n",
      "异常值16\n",
      "异常值8\n",
      "异常值10\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值7\n",
      "异常值24\n",
      "异常值5\n",
      "异常值4\n",
      "异常值16\n",
      "异常值12\n",
      "异常值16\n",
      "异常值24\n",
      "异常值20\n",
      "异常值24\n",
      "异常值4\n",
      "异常值4\n",
      "异常值5\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值6\n",
      "异常值24\n",
      "异常值12\n",
      "异常值24\n",
      "异常值16\n",
      "异常值5\n",
      "异常值15\n",
      "异常值4\n",
      "异常值10\n",
      "异常值24\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值15\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值24\n",
      "异常值4\n",
      "异常值16\n",
      "异常值24\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值24\n",
      "异常值48\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值24\n",
      "异常值24\n",
      "异常值24\n",
      "异常值24\n",
      "异常值32\n",
      "异常值25\n",
      "异常值4\n",
      "异常值5\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值24\n",
      "异常值4\n",
      "异常值12\n",
      "异常值5\n",
      "异常值12\n",
      "异常值4\n",
      "异常值5\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值5\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值5\n",
      "异常值5\n",
      "异常值12\n",
      "异常值3\n",
      "异常值12\n",
      "异常值6\n",
      "异常值12\n",
      "异常值12\n",
      "异常值10\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值24\n",
      "异常值7\n",
      "异常值8\n",
      "异常值5\n",
      "异常值5\n",
      "异常值12\n",
      "异常值4\n",
      "异常值5\n",
      "异常值11\n",
      "异常值5\n",
      "异常值4\n",
      "异常值10\n",
      "异常值9\n",
      "异常值-5\n",
      "异常值5\n",
      "异常值10\n",
      "异常值3\n",
      "异常值4\n",
      "异常值14\n",
      "异常值16\n",
      "异常值7\n",
      "异常值7\n",
      "异常值12\n",
      "异常值5\n",
      "异常值5\n",
      "异常值4\n",
      "异常值16\n",
      "异常值12\n",
      "异常值10\n",
      "异常值4\n",
      "异常值20\n",
      "异常值4\n",
      "异常值5\n",
      "异常值12\n",
      "异常值9\n",
      "异常值5\n",
      "异常值16\n",
      "异常值4\n",
      "异常值10\n",
      "异常值8\n",
      "异常值24\n",
      "异常值4\n",
      "异常值5\n",
      "异常值5\n",
      "异常值5\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值15\n",
      "异常值4\n",
      "异常值5\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值5\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值3\n",
      "异常值10\n",
      "异常值4\n",
      "异常值24\n",
      "异常值18\n",
      "异常值12\n",
      "异常值12\n",
      "异常值-12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值10\n",
      "异常值16\n",
      "异常值12\n",
      "异常值4\n",
      "异常值24\n",
      "异常值24\n",
      "异常值8\n",
      "异常值6\n",
      "异常值9\n",
      "异常值8\n",
      "异常值6\n",
      "异常值50\n",
      "异常值16\n",
      "异常值4\n",
      "异常值7\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值12\n",
      "异常值4\n",
      "异常值15\n",
      "异常值12\n",
      "异常值16\n",
      "异常值3\n",
      "异常值4\n",
      "异常值24\n",
      "异常值6\n",
      "异常值5\n",
      "异常值6\n",
      "异常值4\n",
      "异常值16\n",
      "异常值24\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值6\n",
      "异常值6\n",
      "异常值4\n",
      "异常值12\n",
      "异常值5\n",
      "异常值8\n",
      "异常值4\n",
      "异常值16\n",
      "异常值24\n",
      "异常值15\n",
      "异常值5\n",
      "异常值5\n",
      "异常值4\n",
      "异常值6\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值7\n",
      "异常值6\n",
      "异常值4\n",
      "异常值5\n",
      "异常值5\n",
      "异常值5\n",
      "异常值4\n",
      "异常值7\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值24\n",
      "异常值4\n",
      "异常值5\n",
      "异常值12\n",
      "异常值10\n",
      "异常值24\n",
      "异常值16\n",
      "异常值3\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值5\n",
      "异常值6\n",
      "异常值4\n",
      "异常值5\n",
      "异常值8\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值24\n",
      "异常值12\n",
      "异常值4\n",
      "异常值16\n",
      "异常值5\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值18\n",
      "异常值8\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值8\n",
      "异常值8\n",
      "异常值4\n",
      "异常值18\n",
      "异常值5\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值8\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值10\n",
      "异常值24\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值8\n",
      "异常值6\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值24\n",
      "异常值4\n",
      "异常值5\n",
      "异常值24\n",
      "异常值4\n",
      "异常值9\n",
      "异常值8\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值15\n",
      "异常值6\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值24\n",
      "异常值4\n",
      "异常值16\n",
      "异常值12\n",
      "异常值4\n",
      "异常值10\n",
      "异常值4\n",
      "异常值6\n",
      "异常值6\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值24\n",
      "异常值5\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值8\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值16\n",
      "异常值6\n",
      "异常值8\n",
      "异常值12\n",
      "异常值6\n",
      "异常值6\n",
      "异常值60\n",
      "异常值8\n",
      "异常值216\n",
      "异常值4\n",
      "异常值16\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值5\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值8\n",
      "异常值6\n",
      "异常值12\n",
      "异常值6\n",
      "异常值16\n",
      "异常值15\n",
      "异常值24\n",
      "异常值4\n",
      "异常值3\n",
      "异常值24\n",
      "异常值25\n",
      "异常值10\n",
      "异常值4\n",
      "异常值8\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值24\n",
      "异常值5\n",
      "异常值8\n",
      "异常值4\n",
      "异常值7\n",
      "异常值24\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值12\n",
      "异常值24\n",
      "异常值16\n",
      "异常值5\n",
      "异常值16\n",
      "异常值4\n",
      "异常值12\n",
      "异常值24\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值10\n",
      "异常值16\n",
      "异常值9\n",
      "异常值5\n",
      "异常值12\n",
      "异常值60\n",
      "异常值5\n",
      "异常值15\n",
      "异常值5\n",
      "异常值6\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值24\n",
      "异常值12\n",
      "异常值5\n",
      "异常值12\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值5\n",
      "异常值10\n",
      "异常值4\n",
      "异常值6\n",
      "异常值5\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值6\n",
      "异常值16\n",
      "异常值15\n",
      "异常值10\n",
      "异常值48\n",
      "异常值15\n",
      "异常值24\n",
      "异常值6\n",
      "异常值15\n",
      "异常值10\n",
      "异常值4\n",
      "异常值16\n",
      "异常值12\n",
      "异常值6\n",
      "异常值5\n",
      "异常值10\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值16\n",
      "异常值12\n",
      "异常值24\n",
      "异常值9\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值20\n",
      "异常值12\n",
      "异常值16\n",
      "异常值15\n",
      "异常值5\n",
      "异常值4\n",
      "异常值6\n",
      "异常值12\n",
      "异常值24\n",
      "异常值5\n",
      "异常值12\n",
      "异常值12\n",
      "异常值24\n",
      "异常值16\n",
      "异常值15\n",
      "异常值5\n",
      "异常值20\n",
      "异常值8\n",
      "异常值20\n",
      "异常值5\n",
      "异常值10\n",
      "异常值16\n",
      "异常值12\n",
      "异常值12\n",
      "异常值10\n",
      "异常值5\n",
      "异常值16\n",
      "异常值4\n",
      "异常值6\n",
      "异常值16\n",
      "异常值16\n",
      "异常值9\n",
      "异常值6\n",
      "异常值4\n",
      "异常值7\n",
      "异常值16\n",
      "异常值12\n",
      "异常值24\n",
      "异常值24\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值24\n",
      "异常值5\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值24\n",
      "异常值5\n",
      "异常值4\n",
      "异常值6\n",
      "异常值12\n",
      "异常值7\n",
      "异常值6\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值7\n",
      "异常值4\n",
      "异常值7\n",
      "异常值16\n",
      "异常值20\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值6\n",
      "异常值7\n",
      "异常值24\n",
      "异常值24\n",
      "异常值4\n",
      "异常值10\n",
      "异常值8\n",
      "异常值24\n",
      "异常值16\n",
      "异常值7\n",
      "异常值24\n",
      "异常值24\n",
      "异常值6\n",
      "异常值12\n",
      "异常值12\n",
      "异常值24\n",
      "异常值12\n",
      "异常值7\n",
      "异常值12\n",
      "异常值12\n",
      "异常值10\n",
      "异常值5\n",
      "异常值4\n",
      "异常值5\n",
      "异常值12\n",
      "异常值20\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值24\n",
      "异常值6\n",
      "异常值24\n",
      "异常值5\n",
      "异常值10\n",
      "异常值16\n",
      "异常值6\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值6\n",
      "异常值24\n",
      "异常值48\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值6\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值6\n",
      "异常值24\n",
      "异常值4\n",
      "异常值20\n",
      "异常值4\n",
      "异常值15\n",
      "异常值8\n",
      "异常值4\n",
      "异常值12\n",
      "异常值6\n",
      "异常值16\n",
      "异常值10\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值24\n",
      "异常值12\n",
      "异常值16\n",
      "异常值32\n",
      "异常值12\n",
      "异常值16\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值20\n",
      "异常值24\n",
      "异常值24\n",
      "异常值12\n",
      "异常值12\n",
      "异常值24\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值15\n",
      "异常值5\n",
      "异常值15\n",
      "异常值12\n",
      "异常值6\n",
      "异常值12\n",
      "异常值9\n",
      "异常值12\n",
      "异常值5\n",
      "异常值12\n",
      "异常值24\n",
      "异常值12\n",
      "异常值6\n",
      "异常值12\n",
      "异常值16\n",
      "异常值10\n",
      "异常值12\n",
      "异常值12\n",
      "异常值5\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值5\n",
      "异常值16\n",
      "异常值10\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值10\n",
      "异常值5\n",
      "异常值12\n",
      "异常值6\n",
      "异常值12\n",
      "异常值12\n",
      "异常值6\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值16\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值15\n",
      "异常值24\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值10\n",
      "异常值12\n",
      "异常值4\n",
      "异常值20\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值25\n",
      "异常值12\n",
      "异常值20\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值24\n",
      "异常值4\n",
      "异常值5\n",
      "异常值8\n",
      "异常值25\n",
      "异常值24\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值20\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值15\n",
      "异常值8\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值16\n",
      "异常值24\n",
      "异常值5\n",
      "异常值4\n",
      "异常值16\n",
      "异常值15\n",
      "异常值12\n",
      "异常值4\n",
      "异常值5\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值7\n",
      "异常值16\n",
      "异常值12\n",
      "异常值10\n",
      "异常值10\n",
      "异常值12\n",
      "异常值4\n",
      "异常值16\n",
      "异常值8\n",
      "异常值12\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值15\n",
      "异常值4\n",
      "异常值5\n",
      "异常值16\n",
      "异常值10\n",
      "异常值7\n",
      "异常值10\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值7\n",
      "异常值13\n",
      "异常值24\n",
      "异常值12\n",
      "异常值4\n",
      "异常值10\n",
      "异常值16\n",
      "异常值16\n",
      "异常值12\n",
      "异常值12\n",
      "异常值9\n",
      "异常值12\n",
      "异常值4\n",
      "异常值8\n",
      "异常值5\n",
      "异常值24\n",
      "异常值12\n",
      "异常值10\n",
      "异常值12\n",
      "异常值4\n",
      "异常值16\n",
      "异常值15\n",
      "异常值5\n",
      "异常值8\n",
      "异常值5\n",
      "异常值6\n",
      "异常值15\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值20\n",
      "异常值16\n",
      "异常值4\n",
      "异常值9\n",
      "异常值12\n",
      "异常值75\n",
      "异常值16\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值5\n",
      "异常值6\n",
      "异常值10\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值5\n",
      "异常值20\n",
      "异常值5\n",
      "异常值8\n",
      "异常值4\n",
      "异常值12\n",
      "异常值24\n",
      "异常值4\n",
      "异常值24\n",
      "异常值4\n",
      "异常值12\n",
      "异常值8\n",
      "异常值3\n",
      "异常值10\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值15\n",
      "异常值4\n",
      "异常值4\n",
      "异常值5\n",
      "异常值4\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值15\n",
      "异常值16\n",
      "异常值10\n",
      "异常值4\n",
      "异常值12\n",
      "异常值5\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值16\n",
      "异常值4\n",
      "异常值12\n",
      "异常值8\n",
      "异常值24\n",
      "异常值16\n",
      "异常值18\n",
      "异常值5\n",
      "异常值-16\n",
      "异常值24\n",
      "异常值5\n",
      "异常值4\n",
      "异常值16\n",
      "异常值12\n",
      "异常值9\n",
      "异常值12\n",
      "异常值10\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值15\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值8\n",
      "异常值4\n",
      "异常值9\n",
      "异常值20\n",
      "异常值12\n",
      "异常值10\n",
      "异常值20\n",
      "异常值24\n",
      "异常值16\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值7\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值24\n",
      "异常值24\n",
      "异常值4\n",
      "异常值16\n",
      "异常值16\n",
      "异常值24\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值5\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值5\n",
      "异常值5\n",
      "异常值4\n",
      "异常值12\n",
      "异常值10\n",
      "异常值12\n",
      "异常值4\n",
      "异常值7\n",
      "异常值24\n",
      "异常值4\n",
      "异常值6\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值10\n",
      "异常值16\n",
      "异常值15\n",
      "异常值4\n",
      "异常值4\n",
      "异常值24\n",
      "异常值4\n",
      "异常值10\n",
      "异常值24\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值-2\n",
      "异常值12\n",
      "异常值16\n",
      "异常值24\n",
      "异常值10\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值24\n",
      "异常值10\n",
      "异常值6\n",
      "异常值4\n",
      "异常值12\n",
      "异常值30\n",
      "异常值6\n",
      "异常值6\n",
      "异常值12\n",
      "异常值7\n",
      "异常值15\n",
      "异常值24\n",
      "异常值24\n",
      "异常值4\n",
      "异常值8\n",
      "异常值12\n",
      "异常值4\n",
      "异常值15\n",
      "异常值24\n",
      "异常值4\n",
      "异常值15\n",
      "异常值24\n",
      "异常值4\n",
      "异常值12\n",
      "异常值16\n",
      "异常值12\n",
      "异常值16\n",
      "异常值4\n",
      "异常值4\n",
      "异常值8\n",
      "异常值24\n",
      "异常值10\n",
      "异常值7\n",
      "异常值4\n",
      "异常值24\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值15\n",
      "异常值15\n",
      "异常值6\n",
      "异常值6\n",
      "异常值24\n",
      "异常值24\n",
      "异常值4\n",
      "异常值4\n",
      "异常值20\n",
      "异常值24\n",
      "异常值4\n",
      "异常值4\n",
      "异常值15\n",
      "异常值4\n",
      "异常值24\n",
      "异常值12\n",
      "异常值8\n",
      "异常值20\n",
      "异常值4\n",
      "异常值15\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值10\n",
      "异常值4\n",
      "异常值5\n",
      "异常值15\n",
      "异常值24\n",
      "异常值4\n",
      "异常值6\n",
      "异常值12\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值16\n",
      "异常值4\n",
      "异常值12\n",
      "异常值24\n",
      "异常值5\n",
      "异常值6\n",
      "异常值12\n",
      "异常值16\n",
      "异常值12\n",
      "异常值16\n",
      "异常值4\n",
      "异常值13\n",
      "异常值10\n",
      "异常值5\n",
      "异常值4\n",
      "异常值5\n",
      "异常值15\n",
      "异常值4\n",
      "异常值16\n",
      "异常值13\n",
      "异常值9\n",
      "异常值4\n",
      "异常值16\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值10\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值24\n",
      "异常值10\n",
      "异常值4\n",
      "异常值10\n",
      "异常值4\n",
      "异常值6\n",
      "异常值24\n",
      "异常值4\n",
      "异常值20\n",
      "异常值4\n",
      "异常值12\n",
      "异常值16\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值6\n",
      "异常值6\n",
      "异常值16\n",
      "异常值6\n",
      "异常值24\n",
      "异常值12\n",
      "异常值4\n",
      "异常值16\n",
      "异常值9\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值6\n",
      "异常值12\n",
      "异常值15\n",
      "异常值4\n",
      "异常值24\n",
      "异常值16\n",
      "异常值4\n",
      "异常值20\n",
      "异常值4\n",
      "异常值12\n",
      "异常值15\n",
      "异常值4\n",
      "异常值9\n",
      "异常值16\n",
      "异常值12\n",
      "异常值16\n",
      "异常值12\n",
      "异常值12\n",
      "异常值16\n",
      "异常值16\n",
      "异常值5\n",
      "异常值15\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值5\n",
      "异常值20\n",
      "异常值24\n",
      "异常值6\n",
      "异常值4\n",
      "异常值24\n",
      "异常值12\n",
      "异常值48\n",
      "异常值24\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值-4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值24\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值3\n",
      "异常值12\n",
      "异常值5\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值16\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值24\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值24\n",
      "异常值25\n",
      "异常值4\n",
      "异常值6\n",
      "异常值24\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值5\n",
      "异常值6\n",
      "异常值6\n",
      "异常值6\n",
      "异常值4\n",
      "异常值6\n",
      "异常值-2\n",
      "异常值10\n",
      "异常值20\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值7\n",
      "异常值6\n",
      "异常值4\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值5\n",
      "异常值11\n",
      "异常值5\n",
      "异常值6\n",
      "异常值4\n",
      "异常值6\n",
      "异常值6\n",
      "异常值4\n",
      "异常值6\n",
      "异常值24\n",
      "异常值4\n",
      "异常值24\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值12\n",
      "异常值6\n",
      "异常值12\n",
      "异常值12\n",
      "异常值6\n",
      "异常值6\n",
      "异常值4\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值12\n",
      "异常值24\n",
      "异常值3\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值5\n",
      "异常值20\n",
      "异常值16\n",
      "异常值10\n",
      "异常值24\n",
      "异常值12\n",
      "异常值4\n",
      "异常值5\n",
      "异常值4\n",
      "异常值6\n",
      "异常值6\n",
      "异常值6\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值24\n",
      "异常值16\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值5\n",
      "异常值12\n",
      "异常值6\n",
      "异常值6\n",
      "异常值12\n",
      "异常值8\n",
      "异常值4\n",
      "异常值15\n",
      "异常值16\n",
      "异常值4\n",
      "异常值10\n",
      "异常值6\n",
      "异常值10\n",
      "异常值5\n",
      "异常值6\n",
      "异常值6\n",
      "异常值4\n",
      "异常值6\n",
      "异常值16\n",
      "异常值12\n",
      "异常值20\n",
      "异常值6\n",
      "异常值40\n",
      "异常值15\n",
      "异常值12\n",
      "异常值12\n",
      "异常值6\n",
      "异常值32\n",
      "异常值8\n",
      "异常值24\n",
      "异常值12\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值5\n",
      "异常值16\n",
      "异常值6\n",
      "异常值4\n",
      "异常值15\n",
      "异常值4\n",
      "异常值16\n",
      "异常值6\n",
      "异常值16\n",
      "异常值4\n",
      "异常值5\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值3\n",
      "异常值4\n",
      "异常值4\n",
      "异常值16\n",
      "异常值3\n",
      "异常值16\n",
      "异常值12\n",
      "异常值12\n",
      "异常值12\n",
      "异常值10\n",
      "异常值12\n",
      "异常值16\n",
      "异常值6\n",
      "异常值4\n",
      "异常值24\n",
      "异常值6\n",
      "异常值10\n",
      "异常值24\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值4\n",
      "异常值5\n",
      "异常值16\n",
      "异常值16\n",
      "异常值6\n",
      "异常值12\n",
      "异常值4\n",
      "异常值7\n",
      "异常值10\n",
      "异常值6\n",
      "异常值16\n",
      "异常值4\n",
      "异常值16\n",
      "异常值6\n",
      "异常值7\n",
      "异常值5\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值15\n",
      "异常值4\n",
      "异常值5\n",
      "异常值16\n",
      "异常值5\n",
      "异常值15\n",
      "异常值12\n",
      "异常值12\n",
      "异常值10\n",
      "异常值16\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值12\n",
      "异常值10\n",
      "异常值4\n",
      "异常值16\n",
      "异常值6\n",
      "异常值4\n",
      "异常值4\n",
      "异常值24\n",
      "异常值8\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值5\n",
      "异常值12\n",
      "异常值4\n",
      "异常值6\n",
      "异常值10\n",
      "异常值8\n",
      "异常值5\n",
      "异常值-2\n",
      "异常值12\n",
      "异常值12\n",
      "异常值6\n",
      "异常值30\n",
      "异常值10\n",
      "异常值12\n",
      "异常值9\n",
      "异常值12\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值6\n",
      "异常值4\n",
      "异常值24\n",
      "异常值20\n",
      "异常值24\n",
      "异常值12\n",
      "异常值6\n",
      "异常值6\n",
      "异常值12\n",
      "异常值12\n",
      "异常值5\n",
      "异常值4\n",
      "异常值6\n",
      "异常值4\n",
      "异常值20\n",
      "异常值12\n",
      "异常值4\n",
      "异常值4\n",
      "异常值4\n",
      "异常值9\n",
      "异常值5\n",
      "异常值6\n",
      "异常值5\n",
      "异常值4\n",
      "异常值8\n",
      "异常值12\n",
      "异常值4\n",
      "异常值12\n",
      "异常值3\n",
      "异常值16\n",
      "异常值12\n",
      "异常值4\n",
      "异常值6\n",
      "异常值16\n",
      "异常值24\n",
      "异常值24\n",
      "异常值4\n",
      "异常值16\n",
      "异常值24\n",
      "异常值12\n",
      "异常值24\n",
      "异常值5\n",
      "异常值7\n",
      "异常值16\n",
      "异常值5\n",
      "异常值12\n",
      "异常值7\n",
      "异常值5\n",
      "异常值15\n",
      "异常值5\n",
      "异常值24\n",
      "异常值12\n",
      "异常值5\n",
      "异常值6\n",
      "异常值13\n",
      "异常值16\n",
      "异常值12\n",
      "异常值16\n",
      "异常值12\n",
      "异常值24\n",
      "异常值4\n",
      "异常值16\n",
      "异常值4\n",
      "异常值16\n",
      "异常值16\n",
      "异常值16\n",
      "异常值5\n",
      "异常值12\n",
      "异常值10\n",
      "异常值4\n",
      "异常值4\n",
      "异常值6\n",
      "异常值8\n",
      "异常值10\n",
      "异常值15\n",
      "异常值4\n",
      "异常值7\n",
      "异常值24\n",
      "1.1964138068261654\n",
      "2.518524012518357\n",
      "-1.3221102056921914\n",
      "3.714937819344522\n"
     ]
    }
   ],
   "source": [
    "df_mean=new_data.销售数量.mean()\n",
    "df_std=new_data.销售数量.std()\n",
    "df_min=df_mean-df_std\n",
    "df_max=df_mean+df_std\n",
    "for x in new_data.销售数量:\n",
    "    if (x <df_min) | (x >df_max):\n",
    "        print(\"异常值%d\"%x)\n",
    "print(df_mean)\n",
    "print(df_std)\n",
    "print(df_min)\n",
    "print(df_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 商品单价异常值分析\n",
    "df = pd.DataFrame(new_data['商品单价'])\n",
    "df.plot.box(title=\"商品单价异常值分析\")\n",
    "plt.grid(linestyle=\"--\", alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 销售金额异常值分析\n",
    "df = pd.DataFrame(new_data['销售金额'])\n",
    "df.plot.box(title=\"销售金额异常值分析\")\n",
    "plt.grid(linestyle=\"--\", alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#重复记录处理\n",
    "new=new_data.drop_duplicates(keep='first')               #检验行重复，如果有重复的保留第一条记录"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.40000</td>\n",
       "      <td>2.40</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8.30000</td>\n",
       "      <td>8.30</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.000</td>\n",
       "      <td>11.90000</td>\n",
       "      <td>11.90</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>洗化</td>\n",
       "      <td>3018</td>\n",
       "      <td>卫生巾</td>\n",
       "      <td>301802</td>\n",
       "      <td>夜用卫生巾</td>\n",
       "      <td>20150101</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-3018020109</td>\n",
       "      <td>10片</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>包</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8.90000</td>\n",
       "      <td>8.90</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42811</th>\n",
       "      <td>1605</td>\n",
       "      <td>12</td>\n",
       "      <td>蔬果</td>\n",
       "      <td>1201</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>120106</td>\n",
       "      <td>菌菇类</td>\n",
       "      <td>20150430</td>\n",
       "      <td>201504</td>\n",
       "      <td>DW-1201060002</td>\n",
       "      <td>散称</td>\n",
       "      <td>生鲜</td>\n",
       "      <td>千克</td>\n",
       "      <td>0.217</td>\n",
       "      <td>3.90600</td>\n",
       "      <td>18.00</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42812</th>\n",
       "      <td>1572</td>\n",
       "      <td>12</td>\n",
       "      <td>蔬果</td>\n",
       "      <td>1201</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>120102</td>\n",
       "      <td>根茎</td>\n",
       "      <td>20150430</td>\n",
       "      <td>201504</td>\n",
       "      <td>DW-1201020040</td>\n",
       "      <td>散称</td>\n",
       "      <td>生鲜</td>\n",
       "      <td>千克</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.86240</td>\n",
       "      <td>1.96</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42813</th>\n",
       "      <td>1170</td>\n",
       "      <td>30</td>\n",
       "      <td>洗化</td>\n",
       "      <td>3016</td>\n",
       "      <td>纸制品</td>\n",
       "      <td>301603</td>\n",
       "      <td>无芯纸</td>\n",
       "      <td>20150430</td>\n",
       "      <td>201504</td>\n",
       "      <td>DW-3016030007</td>\n",
       "      <td>10卷</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>提</td>\n",
       "      <td>1.000</td>\n",
       "      <td>14.50000</td>\n",
       "      <td>14.50</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42814</th>\n",
       "      <td>2605</td>\n",
       "      <td>12</td>\n",
       "      <td>蔬果</td>\n",
       "      <td>1201</td>\n",
       "      <td>蔬菜</td>\n",
       "      <td>120101</td>\n",
       "      <td>叶菜</td>\n",
       "      <td>20150430</td>\n",
       "      <td>201504</td>\n",
       "      <td>DW-1201010023</td>\n",
       "      <td>散称</td>\n",
       "      <td>生鲜</td>\n",
       "      <td>千克</td>\n",
       "      <td>0.718</td>\n",
       "      <td>1.83808</td>\n",
       "      <td>2.56</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42815</th>\n",
       "      <td>2610</td>\n",
       "      <td>23</td>\n",
       "      <td>酒饮</td>\n",
       "      <td>2317</td>\n",
       "      <td>进口饮料</td>\n",
       "      <td>231702</td>\n",
       "      <td>进口果汁</td>\n",
       "      <td>20150430</td>\n",
       "      <td>201504</td>\n",
       "      <td>DW-2317020131</td>\n",
       "      <td>490ml</td>\n",
       "      <td>联营商品</td>\n",
       "      <td>罐</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>8.00</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42715 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称      销售日期    销售月份  \\\n",
       "1         1    20   粮油  2014   酱菜类  201401      榨菜  20150101  201501   \n",
       "2         2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "3         3    15   日配  1503  冷藏料理  150305   冷藏面食类  20150101  201501   \n",
       "4         4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳  20150101  201501   \n",
       "5         5    30   洗化  3018   卫生巾  301802   夜用卫生巾  20150101  201501   \n",
       "...     ...   ...  ...   ...   ...     ...     ...       ...     ...   \n",
       "42811  1605    12   蔬果  1201    蔬菜  120106     菌菇类  20150430  201504   \n",
       "42812  1572    12   蔬果  1201    蔬菜  120102      根茎  20150430  201504   \n",
       "42813  1170    30   洗化  3016   纸制品  301603     无芯纸  20150430  201504   \n",
       "42814  2605    12   蔬果  1201    蔬菜  120101      叶菜  20150430  201504   \n",
       "42815  2610    23   酒饮  2317  进口饮料  231702    进口果汁  20150430  201504   \n",
       "\n",
       "                商品编码    规格型号  商品类型  单位   销售数量      销售金额   商品单价 是否促销  \n",
       "1      DW-2014010019     60g  一般商品   袋  6.000   3.00000   0.50    否  \n",
       "2      DW-1505020011    150g  一般商品   袋  1.000   2.40000   2.40    否  \n",
       "3      DW-1503050035    500g  一般商品   袋  1.000   8.30000   8.30    否  \n",
       "4      DW-1505020020  100g*8  一般商品   袋  1.000  11.90000  11.90    否  \n",
       "5      DW-3018020109     10片  一般商品   包  1.000   8.90000   8.90    否  \n",
       "...              ...     ...   ...  ..    ...       ...    ...  ...  \n",
       "42811  DW-1201060002      散称    生鲜  千克  0.217   3.90600  18.00    否  \n",
       "42812  DW-1201020040      散称    生鲜  千克  0.440   0.86240   1.96    否  \n",
       "42813  DW-3016030007     10卷  一般商品   提  1.000  14.50000  14.50    是  \n",
       "42814  DW-1201010023      散称    生鲜  千克  0.718   1.83808   2.56    否  \n",
       "42815  DW-2317020131   490ml  联营商品   罐  1.000   8.00000   8.00    否  \n",
       "\n",
       "[42715 rows x 17 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.duplicated().sum()                 #查看是否还有重复值\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 42715 entries, 1 to 42815\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   顾客编号    42715 non-null  int64  \n",
      " 1   大类编码    42715 non-null  int64  \n",
      " 2   大类名称    42715 non-null  object \n",
      " 3   中类编码    42715 non-null  int64  \n",
      " 4   中类名称    42715 non-null  object \n",
      " 5   小类编码    42715 non-null  int64  \n",
      " 6   小类名称    42715 non-null  object \n",
      " 7   销售日期    42715 non-null  int64  \n",
      " 8   销售月份    42715 non-null  int64  \n",
      " 9   商品编码    42715 non-null  object \n",
      " 10  规格型号    42715 non-null  object \n",
      " 11  商品类型    42715 non-null  object \n",
      " 12  单位      42715 non-null  object \n",
      " 13  销售数量    42715 non-null  float64\n",
      " 14  销售金额    42715 non-null  float64\n",
      " 15  商品单价    42715 non-null  float64\n",
      " 16  是否促销    42715 non-null  object \n",
      "dtypes: float64(3), int64(6), object(8)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# 5、其他处理\n",
    "# 将销售日期改为datetime类型\n",
    "new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\anaconda\\lib\\site-packages\\pandas\\core\\indexing.py:966: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "new.loc[:,'销售日期']=pd.to_datetime(new['销售日期'].astype(str),format='%Y-%m-%d', errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   2015-01-01\n",
       "2   2015-01-01\n",
       "3   2015-01-01\n",
       "4   2015-01-01\n",
       "5   2015-01-01\n",
       "Name: 销售日期, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new['销售日期'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 42715 entries, 1 to 42815\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   顾客编号    42715 non-null  int64         \n",
      " 1   大类编码    42715 non-null  int64         \n",
      " 2   大类名称    42715 non-null  object        \n",
      " 3   中类编码    42715 non-null  int64         \n",
      " 4   中类名称    42715 non-null  object        \n",
      " 5   小类编码    42715 non-null  int64         \n",
      " 6   小类名称    42715 non-null  object        \n",
      " 7   销售日期    42713 non-null  datetime64[ns]\n",
      " 8   销售月份    42715 non-null  int64         \n",
      " 9   商品编码    42715 non-null  object        \n",
      " 10  规格型号    42715 non-null  object        \n",
      " 11  商品类型    42715 non-null  object        \n",
      " 12  单位      42715 non-null  object        \n",
      " 13  销售数量    42715 non-null  float64       \n",
      " 14  销售金额    42715 non-null  float64       \n",
      " 15  商品单价    42715 non-null  float64       \n",
      " 16  是否促销    42715 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(3), int64(5), object(8)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "new=new.dropna()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "顾客编号    0\n",
       "大类编码    0\n",
       "大类名称    0\n",
       "中类编码    0\n",
       "中类名称    0\n",
       "小类编码    0\n",
       "小类名称    0\n",
       "销售日期    0\n",
       "销售月份    0\n",
       "商品编码    0\n",
       "规格型号    0\n",
       "商品类型    0\n",
       "单位      0\n",
       "销售数量    0\n",
       "销售金额    0\n",
       "商品单价    0\n",
       "是否促销    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 42713 entries, 1 to 42815\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   顾客编号    42713 non-null  int64         \n",
      " 1   大类编码    42713 non-null  int64         \n",
      " 2   大类名称    42713 non-null  object        \n",
      " 3   中类编码    42713 non-null  int64         \n",
      " 4   中类名称    42713 non-null  object        \n",
      " 5   小类编码    42713 non-null  int64         \n",
      " 6   小类名称    42713 non-null  object        \n",
      " 7   销售日期    42713 non-null  datetime64[ns]\n",
      " 8   销售月份    42713 non-null  int64         \n",
      " 9   商品编码    42713 non-null  object        \n",
      " 10  规格型号    42713 non-null  object        \n",
      " 11  商品类型    42713 non-null  object        \n",
      " 12  单位      42713 non-null  object        \n",
      " 13  销售数量    42713 non-null  float64       \n",
      " 14  销售金额    42713 non-null  float64       \n",
      " 15  商品单价    42713 non-null  float64       \n",
      " 16  是否促销    42713 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(3), int64(5), object(8)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 实验7\n",
    "# 1、对大类名称商品进行分组求和\n",
    "data1=new.groupby('大类名称').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "大类名称\n",
       "休闲    80660.16140\n",
       "冲调    15141.80000\n",
       "家居     6628.70000\n",
       "家电      882.00000\n",
       "文体     2066.10000\n",
       "日配    92912.60248\n",
       "水产     2891.00782\n",
       "洗化    42677.40000\n",
       "烘焙      110.90600\n",
       "熟食     5939.93040\n",
       "粮油    65380.24764\n",
       "肉禽    25197.75280\n",
       "蔬果    82114.08604\n",
       "酒饮    57336.70000\n",
       "针织     6689.90000\n",
       "Name: 销售金额, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2、查看分组求和结果后的销售金额列数据\n",
    "data1=data1['销售金额']\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3、可视化分析——绘制每个大类商品销售金额统计分析图表\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.formula.api import logit\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif']=['SimHei'] #绘图中增加中文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置图框的大小\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "# 绘制饼图，textprops={'fontproperties':font}显示中文\n",
    "plt.pie(x=data1.values,\n",
    "        labels=data1.index,\n",
    "        autopct='%.1f%%',\n",
    "        shadow=False,\n",
    "        startangle=90,\n",
    "        center = (3,3),)\n",
    "\n",
    "# 添加标题，fontproperties=font显示中文\n",
    "plt.title(\"每个大类商品销售金额统计分析\")\n",
    "# 饼图保持圆形\n",
    "plt.axis('equal')\n",
    "# 显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4、每个大类商品销售金额统计分析-柱状图\n",
    "X=data1.index\n",
    "Y=data1.values\n",
    "fig = plt.figure()\n",
    "plt.bar(X,Y,0.4,color=\"blue\")\n",
    "plt.xlabel(\"销售金额\")\n",
    "plt.ylabel(\"大类名称\")\n",
    "plt.title(\"每个大类商品销售金额统计分析\") \n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>中类名称</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>酱菜类</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>2.40000</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>8.30000</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>11.90000</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>卫生巾</td>\n",
       "      <td>8.90000</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42811</th>\n",
       "      <td>蔬菜</td>\n",
       "      <td>3.90600</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42812</th>\n",
       "      <td>蔬菜</td>\n",
       "      <td>0.86240</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42813</th>\n",
       "      <td>纸制品</td>\n",
       "      <td>14.50000</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42814</th>\n",
       "      <td>蔬菜</td>\n",
       "      <td>1.83808</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42815</th>\n",
       "      <td>进口饮料</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42713 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       中类名称      销售金额 是否促销\n",
       "1       酱菜类   3.00000    否\n",
       "2      冷藏乳品   2.40000    否\n",
       "3      冷藏料理   8.30000    否\n",
       "4      冷藏乳品  11.90000    否\n",
       "5       卫生巾   8.90000    否\n",
       "...     ...       ...  ...\n",
       "42811    蔬菜   3.90600    否\n",
       "42812    蔬菜   0.86240    否\n",
       "42813   纸制品  14.50000    是\n",
       "42814    蔬菜   1.83808    否\n",
       "42815  进口饮料   8.00000    否\n",
       "\n",
       "[42713 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 实验8\n",
    "# 1、筛选出中类名称、销售金额、是否促销列\n",
    "data3=new[['中类名称','销售金额','是否促销']]\n",
    "data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>中类名称</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>毯子</td>\n",
       "      <td>90.0000</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>饼干</td>\n",
       "      <td>3.9204</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>常温乳品</td>\n",
       "      <td>43.2000</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>南北干货</td>\n",
       "      <td>11.8536</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>袋装速食面组</td>\n",
       "      <td>14.7000</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42797</th>\n",
       "      <td>南北干货</td>\n",
       "      <td>44.9000</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42807</th>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>14.9000</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42809</th>\n",
       "      <td>糕点</td>\n",
       "      <td>6.5000</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42810</th>\n",
       "      <td>卫生巾</td>\n",
       "      <td>4.7000</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42813</th>\n",
       "      <td>纸制品</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6496 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         中类名称     销售金额 是否促销\n",
       "18         毯子  90.0000    是\n",
       "19         饼干   3.9204    是\n",
       "22       常温乳品  43.2000    是\n",
       "27       南北干货  11.8536    是\n",
       "34     袋装速食面组  14.7000    是\n",
       "...       ...      ...  ...\n",
       "42797    南北干货  44.9000    是\n",
       "42807    冷藏乳品  14.9000    是\n",
       "42809      糕点   6.5000    是\n",
       "42810     卫生巾   4.7000    是\n",
       "42813     纸制品  14.5000    是\n",
       "\n",
       "[6496 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2、筛选出是促销的中类名称\n",
    "data4=data3[data3['是否促销']=='是']\n",
    "data4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中类名称</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一次性用品</th>\n",
       "      <td>94.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乳饮料</th>\n",
       "      <td>788.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>五谷杂粮</th>\n",
       "      <td>3498.83888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>保养用品</th>\n",
       "      <td>95.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>保温容器</th>\n",
       "      <td>153.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>酱菜类</th>\n",
       "      <td>231.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>鞋类护理用品</th>\n",
       "      <td>13.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>食用油</th>\n",
       "      <td>9306.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>饼干</th>\n",
       "      <td>3616.69120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>香/烛</th>\n",
       "      <td>16.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              销售金额\n",
       "中类名称              \n",
       "一次性用品     94.80000\n",
       "乳饮料      788.30000\n",
       "五谷杂粮    3498.83888\n",
       "保养用品      95.30000\n",
       "保温容器     153.00000\n",
       "...            ...\n",
       "酱菜类      231.30000\n",
       "鞋类护理用品    13.90000\n",
       "食用油     9306.30000\n",
       "饼干      3616.69120\n",
       "香/烛       16.00000\n",
       "\n",
       "[109 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3、对是促销的中类名称商品按‘中类名称’分类求和\n",
    "data5=data4.groupby('中类名称').sum()\n",
    "data5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中类名称</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>常温乳品</th>\n",
       "      <td>32006.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>纸制品</th>\n",
       "      <td>11096.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国产白酒</th>\n",
       "      <td>10423.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>糕点</th>\n",
       "      <td>10300.73720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>食用油</th>\n",
       "      <td>9306.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>冷藏乳品</th>\n",
       "      <td>7597.10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>膨化点心</th>\n",
       "      <td>5235.81200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>饼干</th>\n",
       "      <td>3616.69120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>炒货</th>\n",
       "      <td>3536.18400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>五谷杂粮</th>\n",
       "      <td>3498.83888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             销售金额\n",
       "中类名称             \n",
       "常温乳品  32006.40000\n",
       "纸制品   11096.40000\n",
       "国产白酒  10423.90000\n",
       "糕点    10300.73720\n",
       "食用油    9306.30000\n",
       "冷藏乳品   7597.10000\n",
       "膨化点心   5235.81200\n",
       "饼干     3616.69120\n",
       "炒货     3536.18400\n",
       "五谷杂粮   3498.83888"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4、可视化分析--对是促销的中类名称商品销售额前10进行统计分析\n",
    "data5_cuxiao=data5.sort_values(by='销售金额',ascending=False)[:10]\n",
    "data5_cuxiao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-34-026f98616a23>:4: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n",
      "  plt.pie(x=data5_cuxiao.values,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置图框的大小\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "# 绘制饼图，textprops={'fontproperties':font}显示中文\n",
    "plt.pie(x=data5_cuxiao.values,\n",
    "        labels=data5_cuxiao.index,\n",
    "        autopct='%.1f%%',\n",
    "        shadow=False,\n",
    "        startangle=90,\n",
    "        center = (3,3),)\n",
    "\n",
    "# 添加标题，fontproperties=font显示中文\n",
    "plt.title(\"中类促销商品销售金额前10所占比例\")\n",
    "# 饼图保持圆形\n",
    "plt.axis('equal')\n",
    "# 显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中类名称</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>水果</th>\n",
       "      <td>45285.12650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>蔬菜</th>\n",
       "      <td>33957.33198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>猪肉</th>\n",
       "      <td>19779.08560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>香烟</th>\n",
       "      <td>15132.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>常温乳品</th>\n",
       "      <td>13753.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>蛋类</th>\n",
       "      <td>13263.39848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>国产白酒</th>\n",
       "      <td>11699.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>五谷杂粮</th>\n",
       "      <td>9840.60676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>饼干</th>\n",
       "      <td>9837.32040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>炒货</th>\n",
       "      <td>6015.80120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             销售金额\n",
       "中类名称             \n",
       "水果    45285.12650\n",
       "蔬菜    33957.33198\n",
       "猪肉    19779.08560\n",
       "香烟    15132.00000\n",
       "常温乳品  13753.40000\n",
       "蛋类    13263.39848\n",
       "国产白酒  11699.00000\n",
       "五谷杂粮   9840.60676\n",
       "饼干     9837.32040\n",
       "炒货     6015.80120"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  实验9\n",
    "# 5、对非促销的中类名称商品的销售金额前10统计分析\n",
    "data6=data3[data3['是否促销']=='否']\n",
    "data6=data6.groupby('中类名称').sum()\n",
    "data7=data6.sort_values(by='销售金额',ascending=False)[:10]\n",
    "data7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-36-f67f53a22074>:4: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n",
      "  plt.pie(x=data7.values,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置图框的大小\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "# 绘制饼图，textprops={'fontproperties':font}显示中文\n",
    "plt.pie(x=data7.values,\n",
    "        labels=data7.index,\n",
    "        autopct='%.1f%%',\n",
    "        shadow=False,\n",
    "        startangle=90,\n",
    "        center = (3,3),)\n",
    "\n",
    "# 添加标题，fontproperties=font显示中文\n",
    "plt.title(\"中类非促销商品销售金额前10所占比例\")\n",
    "# 饼图保持圆形\n",
    "plt.axis('equal')\n",
    "# 显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>洗化</td>\n",
       "      <td>3018</td>\n",
       "      <td>卫生巾</td>\n",
       "      <td>301802</td>\n",
       "      <td>夜用卫生巾</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-3018020109</td>\n",
       "      <td>10片</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>包</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称       销售日期    销售月份  \\\n",
       "1     1    20   粮油  2014   酱菜类  201401      榨菜 2015-01-01  201501   \n",
       "2     2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳 2015-01-01  201501   \n",
       "3     3    15   日配  1503  冷藏料理  150305   冷藏面食类 2015-01-01  201501   \n",
       "4     4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳 2015-01-01  201501   \n",
       "5     5    30   洗化  3018   卫生巾  301802   夜用卫生巾 2015-01-01  201501   \n",
       "\n",
       "            商品编码    规格型号  商品类型 单位  销售数量  销售金额  商品单价 是否促销  \n",
       "1  DW-2014010019     60g  一般商品  袋   6.0   3.0   0.5    否  \n",
       "2  DW-1505020011    150g  一般商品  袋   1.0   2.4   2.4    否  \n",
       "3  DW-1503050035    500g  一般商品  袋   1.0   8.3   8.3    否  \n",
       "4  DW-1505020020  100g*8  一般商品  袋   1.0  11.9  11.9    否  \n",
       "5  DW-3018020109     10片  一般商品  包   1.0   8.9   8.9    否  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 实验10  统计分析每位顾客每月的消费次数\n",
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 42713 entries, 1 to 42815\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   顾客编号    42713 non-null  int64         \n",
      " 1   大类编码    42713 non-null  int64         \n",
      " 2   大类名称    42713 non-null  object        \n",
      " 3   中类编码    42713 non-null  int64         \n",
      " 4   中类名称    42713 non-null  object        \n",
      " 5   小类编码    42713 non-null  int64         \n",
      " 6   小类名称    42713 non-null  object        \n",
      " 7   销售日期    42713 non-null  datetime64[ns]\n",
      " 8   销售月份    42713 non-null  int64         \n",
      " 9   商品编码    42713 non-null  object        \n",
      " 10  规格型号    42713 non-null  object        \n",
      " 11  商品类型    42713 non-null  object        \n",
      " 12  单位      42713 non-null  object        \n",
      " 13  销售数量    42713 non-null  float64       \n",
      " 14  销售金额    42713 non-null  float64       \n",
      " 15  商品单价    42713 non-null  float64       \n",
      " 16  是否促销    42713 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(3), int64(5), object(8)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>销售日期</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42811</th>\n",
       "      <td>1605</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42812</th>\n",
       "      <td>1572</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42813</th>\n",
       "      <td>1170</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42814</th>\n",
       "      <td>2605</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42815</th>\n",
       "      <td>2610</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42713 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       顾客编号       销售日期\n",
       "1         1 2015-01-01\n",
       "2         2 2015-01-01\n",
       "3         3 2015-01-01\n",
       "4         4 2015-01-01\n",
       "5         5 2015-01-01\n",
       "...     ...        ...\n",
       "42811  1605 2015-04-30\n",
       "42812  1572 2015-04-30\n",
       "42813  1170 2015-04-30\n",
       "42814  2605 2015-04-30\n",
       "42815  2610 2015-04-30\n",
       "\n",
       "[42713 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_month=new[['顾客编号','销售日期']]\n",
    "data_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-4676a82703e3>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_month['月份']=[x.month for x in data_month['销售日期']]\n",
      "D:\\Anaconda\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py:3990: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>月份</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  月份\n",
       "1     1   1\n",
       "2     2   1\n",
       "3     3   1\n",
       "4     4   1\n",
       "5     5   1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_month['月份']=[x.month for x in data_month['销售日期']]\n",
    "data_month.drop(['销售日期'],axis=1,inplace=True)\n",
    "data_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拆分出四个月的表\n",
    "data_month1 = data_month.loc[data_month['月份'] == 1,:]\n",
    "data_month2 = data_month.loc[data_month['月份'] == 2,:]\n",
    "data_month3 = data_month.loc[data_month['月份'] == 3,:]\n",
    "data_month4 = data_month.loc[data_month['月份'] == 4,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据顾客编号列，求出每位顾客每月的消费次数\n",
    "data_month1_times = data_month1['顾客编号'].value_counts(dropna=False)\n",
    "data_month2_times = data_month2['顾客编号'].value_counts(dropna=False)\n",
    "data_month3_times = data_month3['顾客编号'].value_counts(dropna=False)\n",
    "data_month4_times = data_month4['顾客编号'].value_counts(dropna=False)\n",
    "\n",
    "data_month1_times = pd.DataFrame({'顾客编号':data_month1_times.index, '次数':data_month1_times.values})\n",
    "data_month2_times = pd.DataFrame({'顾客编号':data_month2_times.index, '次数':data_month2_times.values})\n",
    "data_month3_times = pd.DataFrame({'顾客编号':data_month3_times.index, '次数':data_month3_times.values})\n",
    "data_month4_times = pd.DataFrame({'顾客编号':data_month4_times.index, '次数':data_month4_times.values})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>次数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>1189</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>1193</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>1208</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>873</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>1006</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1225 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      顾客编号   次数\n",
       "0       51  115\n",
       "1       92   98\n",
       "2       47   89\n",
       "3      108   86\n",
       "4       12   77\n",
       "...    ...  ...\n",
       "1220  1189    1\n",
       "1221  1193    1\n",
       "1222  1208    1\n",
       "1223   873    1\n",
       "1224  1006    1\n",
       "\n",
       "[1225 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_month1_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1月消费次数</th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>2月消费次数</th>\n",
       "      <th>3月消费次数</th>\n",
       "      <th>4月消费次数</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>115</td>\n",
       "      <td>32.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>89</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>86</td>\n",
       "      <td>18.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>77</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1月消费次数  顾客编号  2月消费次数  3月消费次数  4月消费次数\n",
       "0      51   115    32.0    19.0    38.0\n",
       "1      92    98    28.0     0.0     0.0\n",
       "2      47    89     7.0    63.0    34.0\n",
       "3     108    86    18.0    43.0    29.0\n",
       "4      12    77    30.0    47.0    25.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将四个消费次数表连接\n",
    "data_month12_times = pd.merge(data_month1_times, data_month2_times, how='left', left_on='顾客编号', right_on='顾客编号')\n",
    "data_month34_times = pd.merge(data_month3_times, data_month4_times, how='left', left_on='顾客编号', right_on='顾客编号')\n",
    "data_month1234_times = pd.merge(data_month12_times, data_month34_times, how='left', left_on='顾客编号', right_on='顾客编号')\n",
    "data_month1234_times.columns = list(['1月消费次数', '顾客编号', '2月消费次数', '3月消费次数', '4月消费次数'])\n",
    "data_month1234_times.fillna(0, inplace=True)\n",
    "data_month1234_times.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>顾客编号</th>\n",
       "      <th>2月消费次数</th>\n",
       "      <th>3月消费次数</th>\n",
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       "      <td>32.0</td>\n",
       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>89</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>86</td>\n",
       "      <td>18.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>77</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>1189</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>1193</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>1208</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>873</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>1006</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1225 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      1月消费次数  顾客编号  2月消费次数  3月消费次数  4月消费次数\n",
       "0         51   115    32.0    19.0    38.0\n",
       "1         92    98    28.0     0.0     0.0\n",
       "2         47    89     7.0    63.0    34.0\n",
       "3        108    86    18.0    43.0    29.0\n",
       "4         12    77    30.0    47.0    25.0\n",
       "...      ...   ...     ...     ...     ...\n",
       "1220    1189     1     0.0     0.0     0.0\n",
       "1221    1193     1     0.0     0.0     0.0\n",
       "1222    1208     1     0.0     0.0     0.0\n",
       "1223     873     1     0.0     2.0     0.0\n",
       "1224    1006     1     0.0     0.0     0.0\n",
       "\n",
       "[1225 rows x 5 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_month1234_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将顾客编号设置为行标签\n",
    "data_month1234_times.set_index('顾客编号',inplace=True)\n",
    "data_month1234_times.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1月消费次数</th>\n",
       "      <th>2月消费次数</th>\n",
       "      <th>3月消费次数</th>\n",
       "      <th>4月消费次数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>顾客编号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>51</td>\n",
       "      <td>32.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>92</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>47</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>108</td>\n",
       "      <td>18.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>12</td>\n",
       "      <td>30.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1月消费次数  2月消费次数  3月消费次数  4月消费次数\n",
       "顾客编号                                \n",
       "115       51    32.0    19.0    38.0\n",
       "98        92    28.0     0.0     0.0\n",
       "89        47     7.0    63.0    34.0\n",
       "86       108    18.0    43.0    29.0\n",
       "77        12    30.0    47.0    25.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_month1234_times['总消费次数']=data_month1234_times.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_xiaofei=data_month1234_times.sort_values(by='总消费次数',ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1月消费次数</th>\n",
       "      <th>2月消费次数</th>\n",
       "      <th>3月消费次数</th>\n",
       "      <th>4月消费次数</th>\n",
       "      <th>总消费次数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>顾客编号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1215</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1204</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1212</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1216</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1211</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1133</td>\n",
       "      <td>48.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1227.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1187</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1221</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1200</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1223.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1月消费次数  2月消费次数  3月消费次数  4月消费次数   总消费次数\n",
       "顾客编号                                        \n",
       "7       1215    44.0     5.0     0.0  1264.0\n",
       "5       1204    24.0    10.0    15.0  1253.0\n",
       "11      1212     4.0    14.0    16.0  1246.0\n",
       "2       1216    22.0     4.0     0.0  1242.0\n",
       "2       1211     0.0    15.0    13.0  1239.0\n",
       "20      1133    48.0    22.0    25.0  1228.0\n",
       "2       1224     0.0     3.0     0.0  1227.0\n",
       "17      1187     1.0    15.0    22.0  1225.0\n",
       "5       1221     4.0     0.0     0.0  1225.0\n",
       "8       1200    20.0     3.0     0.0  1223.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_xiaofei"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1月消费次数    1215.0\n",
       "2月消费次数      44.0\n",
       "3月消费次数       5.0\n",
       "4月消费次数       0.0\n",
       "Name: 7, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可视化\n",
    "data_xiaofei.iloc[0,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([7, 5, 11, 2, 2, 20, 2, 17, 5, 8], dtype='int64', name='顾客编号')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_xf=data_xiaofei.index\n",
    "data_xf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hy867aFq/eqDDUkpdpGAtf71hwwYGDhzI2LFjs9uOHTt2VvnrYFGghGCMqQHMFJE4e3gScDUwV0SevZi2YBPX4yUe39CdwdMzWDL2DZq++VSgQ1KqxBr701i2Ht1apMu8qupVPNL+kfNOE6zlrxMTE/nqq69YunQpLVq04O233863/HWwuGBCMMZUAT4AIuzhmwCHiHQyxkw2xjQCWhSkTUQK/hSnmJjKV9C3XQLrVi2n3Q9f8NOmYbRvrq/aVKokCdby1+3atWPx4sVcfvnlDB48mHnz5tGnT5/i2SmFUJArBA/QH5htDycA0+3+RUAs0LqAbUGXEAA6dn+B2Ws702qKi8UvvUK791/S8thKFcKF/pL3h2Auf52RkUFYWBgAMTExF/XJpkC44ENlETkpIidyNUUAe+3+o0CNi2jLwxgzwhiz2hizOvd9t2JXviq3dbiBH5sYuqxZwJKfgvugKaVyZJW/Bs4qf920adM85a9r1arFzp07mTBhAk8//TS//PIL27dv55133iEhIQGHw8Hw4cMZPHgwy5cvzy5//f7777NgwQIee+yxPOvOXf46q4uMjMx+PjBo0CDWr1+Px+Nh1qxZREdHF/v+uRiF+ZRRKlDO7q9gL6OgbXmIyEQRiRGRmKioqEKEUnRaJ/yblI5CmMvD5vH/0/LYSpUQwVz++sknn2TQoEG0atWKTp060aNHj0DsogIrzKeMkvDd/kkEooFtwJ4CtgWvsArc3mkgy5Om0nXTMuYsWc+NPVoFOiql1AU8/PDDVKxYkSeffJLu3bszb948Xn/9daZPn06lSpUYMWIEbdu2JSYmBpfLxQMPPEDlypWz51+3bh2pqalERkaSnJxMZGQkkyZNIiIigtmzZ1OlShV69+5NbGwsM2bMYOjQoWfFsHv3bnr27Em5cuUYPHhwdnvz5s3ZsGFDvnEvW7asyPfFpSpw+WtjzDIRSTDGVASWA98A1wEdASlI2xm3nvIolvLXF+LO4F//a8Ntk71816gjwz57lzBnya9gqJQ/aflrnzJV/lpEEuyfJ/E9WE4EuonIiYK2FUXAfuUMY3CXESyJNsQnJzJzzo+BjkgpdZG0/HXhFWrPiMgxEZkuIvsvti3YNeowmmMdQvA44NTEV/kj3RXokJRSqliU6dIV+bIc3B3/IAvbGjrvWM/U6d8GOiKllCoWmhDyUa/VEFzty5MRCiEfvs7h1IwLz6SUCjitdnppynQto3MyhruveYJ31z7BrSu2MeWDBdx/X/B9zVwp5aPVTouGXiGcQ62m/QhrX5k/ykH1GW+y+6i+REepYBWs1U6zHDhwgNatWwO+AnlZX2Jr1aoVf/3rX4thDxWMXiGcxz09nuXVtaO5felvfPrOLP7xyO2BDkkplY9grXaa5eGHH86uqzRy5EhGjhwJwJgxYxgyZEhR745C04RwHlENr6Fqh5ocXfU7jee8w5bB19P0cn2JjlLnsv/558nYUrTVTsOaXkXNxx8/7zTBWu3UsiyWLFlCRETEWVcOe/fu5cCBA8TEFMlXCIqEJoQLGNJjLGPXDuGuhfv5bMIM/vnMsECHpJQ6Q7BWO73xxht55pln+OKLL7jxxhvzTPPGG29kXykEC00IF1D1inbUaV+f/T/uIObr91h19420a1gt0GEpFZQu9Je8PwRztdOnn36aUaNG5SmVAb4rmqVLl/Lcc88Vxy4qME0IBTC413j+vfYmRnx1lGmvfULM+Pu1PLZSQSKr2unWrVvPqnbau3fvPNVOjx8/TmJiIhMmTMief/v27SQmJlK+fHlEhOHDh9O1a1fGjRuXXe006wohKSmJRx7JKfGdu9pplqSkJPbv309ERASLFy9myZIlvPHGG6xbt45hw4bx7rvvsnz5cjp06BB05xFNCAVQMeoqru5wNbsSfyZ+xScs+fkWujevHeiwlFL4qp1ec801fP311zz33HO899579OnTh1deeSVPtdO4uDg2bdrEuHHjznou0LhxY4wx7NixI0+102+++YYxY8Ywf/58tm3bxqBBgzh+/Hj2vN27d6d79+7Zw5mZmXTq1Cm72ul3332XZz3vvvsuAAsXLqRr165+3jMXTxNCAd3R6788sqY3o774gymvfUjCm4/isIIruytVFgVztdPcclc3ff7554t8PxSFAlc79begqHZ6AR/MHkzV/62i6olyHHxzJv06NAx0SEoFnFY79SlT1U4V9O8xnvlxhqqnTpM04T0y3J4Lz6SUKlZa7bTwNCFchPCIasR3jGdTPUOf9V8y9bvkQIekVFAIljsNpVlx7GNNCBfppmteZHGsITI9kx1vTyI1wx3okJQKqPDwcI4cOaJJwY9EhCNHjhAeHu7X9ehD5YsUEh7JtZ2vJ+nHr+izZQHvL7yd0X3aBDospQLmiiuuYM+ePfmWbFBFJzw8nCuuuMKv69CEUAg3dH2aUavn0/Y9N8ffm8yRa5pxWYWwQIelVECEhITQoEGDQIehioDeMioEZ0g4/Tr35/umhhtSlvLOl8H96SillCoITQiF1LvLY/wY68Th8eKYMlnLYyulSjxNCIVkWQ4Gxg1lWUvDtb+uZOLMHwIdklJKXRJNCJegW7v72dAlDIxQ47PJbNv/R6BDUkqpQtOEcAmMMQyJ/xsLWxu67VrLu1OWBTokpZQqNE0Il6hzyyH80rkCmU64at57rN55NNAhKaVUoWhCuETGGIZ2f4y57Qyd92zh/Y++1i/oKKVKpItOCMaYKsaYecaY1caYt+22ScaYlcaYf+aa7qy20iqmST8OdKlCWhh0+eZDlm47GOiQlFLqohXmCmEQ8IldXS/SGPMPwCEinYCGxphGxpibzmwrwpiD0vBu/2Z2R4tW+39l2vvz8Hr1KkEpVbIUJiEcAZobYyoDdYAGwHR73CIgFkjIp+0sxpgR9pXG6pL+tfeWDXuR2rk6J8rDdd9/zOx1ewIdklJKXZTCJIQVQD3gfmALEArstccdBWoAEfm0nUVEJopIjIjEREVFFSKU4HJv9xf4rItFk0P7mPfBHDLd3kCHpJRSBVaYhPBv4F4ReRrYCtwOlLPHVbCXmZpPW6nX5IqOmM71OFwRbvnpUz798bdAh6SUUgVWmBN1FaCFMcYBdAD+j5xbQtHATiApn7Yy4d7u/8eMWEPdY4f54cPPSdPy2EqpEqIwCeEFYCJwAqgK/BcYZIwZD9wGzAVm5dNWJjSs3pKKna5iX1UYuHY6k77bHuiQlFKqQC46IYjITyLSTEQqiEhPETmJ7yFyItBNRE7k11aUQQe7v/Z4kRlxhhonT7L54884kpoR6JCUUuqCiuTevogcE5HpIrL/fG1lRZ0qf6JW59bsrA6DN33Om99sC3RISil1QWXiYW8gjOj2IjPiLaqkneLg1OnsOablsZVSwU0Tgp/UrFibJp06sfUKGLT1K16b/3OgQ1JKqfPShOBHQ+Nf4POuDiqczkA+n0ryAS2PrZQKXpoQ/KhaRBRtO/dgfQMYmLKIV75cF+iQlFLqnDQh+Nndsf9hdlcH4Rluqs2dStJvxwIdklJK5UsTgp9VCq9M1y79+LGJ4eZflvHqF6u1PLZSKihpQigGgzo+wtyuTpwuD80WT2VZcsku5KeUKp00IRSDCqEVuK7L7XzX3OLPO1by1mc/anlspVTQ0YRQTAa0vZ/FcaEgXuKWf8qcDfsCHZJSSuWhCaGYlA8pT7/YYSyOtui2cy0fzPxBy2MrpYKKJoRidGurESyPK4fHEm74cSpTV+0KdEhKKZVNE0IxCnWEMjBuNPNiLDrs3sxnny3X8thKqaChCaGY9W12J6tjI8kIFW5fM5XJK3YEOiSllAI0IRS7ECuEIXF/58v2Dlru+5XFs5ZxNC0z0GEppZQmhED4c+Ob+blLZdLChWHrpzFhqb5ERykVeJoQAsBhORjW9TE+6+zgTwf3kjTnG/YePx3osJRSZZwmhADp0fDP7OxcnRMVhPs2Ted/i/QlOkqpwNKEECCWsRgZ9y+mxTqofeQwu+YvIkXLYyulAkgTQgDF1e3G4U51OFQJxmz5nJcWbAl0SEqpMkwTQgAZY7gv9t9M7Wpx2fETZC5eyJpdWh5bKRUYmhACrEOtjmR0bMS+ajBq2yxemvuzlsdWSgWEJoQAM8YwOvZJPom3iDx5iqrfzeNbLY+tlAoATQhBoHWNNoR1aMGOmjA0eR7j5m7S8thKqWJ3SQnBGDPBGHOD3T/JGLPSGPPPXOPPalP5G93lX3ycYFEuLYOmK7/S8thKqWJX6IRgjIkDaorIHGPMTYBDRDoBDY0xjfJrK6KYS6VmlzWjRocObKkLd6R8zWtzN2p5bKVUsSpUQjDGhADvADuNMX2BBGC6PXoREHuONnUeozo+xpR4B6HpbuKSvmSalsdWShWjwl4hDAY2Ay8C7YH7gL32uKNADSAin7Y8jDEjjDGrjTGrDx3SB6mNqjTiyg4JrL0Sbt7+Le/M38CpTC2PrZQqHoVNCK2BiSKyH/gY+A4oZ4+rYC83NZ+2PERkoojEiEhMVFRUIUMpXUa1/39MjXfiyPTSd/0XvPf9zkCHpJQqIwqbELYDDe3+GKA+ObeEooGdQFI+beoC6lWsR+v217GyqeHaX37k0wXrOKblsZVSxaCwCWES0M0Y8x0wCt/zgkHGmPHAbcBcYFY+baoA/hrzADPiHODxMmjTTN789pdAh6SUKgMKlRBE5A8RuVVEuopIJxH5DV9SSAS6icgJETl5ZltRBV3a1apQi9gON/FtC4u4Hev5avFa9ml5bKWUnxXZF9NE5JiITLefK5yzTRXM8Nb3MTsuBC/CfVum88rilECHpJQq5fSbykGqevnq9Go/kIVtLFrv3Mb3y5LYflDLYyul/EcTQhC7O3oEC7qE4XYK/y95Oi8vTA50SEqpUkwTQhCrGl6VPu3vYk47i8a7dpL8w2rWanlspZSfaEIIckNa3M3STuVJDxMe2zaNsQu2anlspZRfaEIIchVDK9K//Qg+7+jgin2/k7p6FctTDgc6LKVUKaQJoQS44+o7WdmxAmnlhUe3TWfs/C1aHlspVeQ0IZQA5UPKM6j9KKZ1cXDZgSNErE9k7sbfAx2WUqqU0YRQQvRv0p8N7atwIlJ4OPkzxi3cisuj5bGVUkVHE0IJEe4M5552Y5gS5yDy8EkabvqOaat2BzospVQpogmhBLmp0U2kxERxuIowJmUOr369TctjK6WKjCaEEiTEEcKIdn/jo3gH4cdO0XHrEi2PrZQqMpoQSpgbrryB/W1qsy9KGJ48n3eWJnP8lJbHVkpdOk0IJYzTcjKy3YN8kODA+Ucm1ycv4M1lWh5bKXXpNCGUQL3r9+aP1g3YWQtuT17KlOXJ/H5Cy2MrpS6NJoQSyDIWo2Me5L1uDqxTbgZtn6flsZVSl0wTQgl1TZ1rsKKbsrU+/CVlBXNWprD9YGqgw1JKlWCaEEooYwxj2j7A+wkOTLqXMTtmMW7RtkCHpZQqwTQhlGCda3WmcstWrGsE8clJrFiznfW7jwc6LKVUCaUJoQQzxjC6zd/4MN6BuLw8tmOmlsdWShWaJoQSrl3NdtRp0ZHEZhatkzexbdN2VmzX8thKqYunCaEUGN3mfj6Js/CK8OSO6by4YJuWx1ZKXTRNCKVAy6iWXN0igW+jLRqnpHA0OZl5m7Q8tlLq4mhCKCXuaz2aqV0sPJbw1K9TGbcoWctjK6UuiiaEUuKqqlfRvvm1LGrroPYvu3D8upXpq7U8tlKq4AqdEIwxNYwxa+3+ScaYlcaYf+Yaf1ab8q9RrUbxeScLVwg89etUXlmcwulMT6DDUkqVEJdyhfAyUM4YcxPgEJFOQENjTKP82ooiWHV+DSs3JL5FH+Z0cFB15wFq7drIez/sCHRYSqkSolAJwRhzDZAG7AcSgOn2qEVA7Dna8lvOCGPMamPM6kOHDhUmFHWGe6PvZW4HJxnhwr9+mcFby37hxClXoMNSSpUAF50QjDGhwL+AR+2mCGCv3X8UqHGOtrOIyEQRiRGRmKioqIsNReWjTmQdrm1+MzM6Oyi/5xgt967hzW+1PLZS6sIKc4XwKDBBRLJqJKQC5ez+CvYy82tTxWREyxF8ExPKqQjhHzF3H7kAABQRSURBVCmf896KX9l/Ij3QYSmlglxhTtQ9gPuMMcuAVsAN5NwSigZ2Akn5tKliUjOiJv2aD+DTWAch+1Pp+ftKXvlGy2Mrpc7PebEziEjXrH47KfQBlhtjagHXAR0ByadNFaOhLYZyQ5vp3LoyjZHJc+m7qgPD4hpwZVSFQIemlApSl3QrR0QSROQkvofIiUA3ETmRX9ulBqouTrVy1ejffBAfxDuwDp+m//5ljF+UHOiwlFJBrEju7YvIMRGZLiL7z9emitfdze9mXcsKHL1MuHPLIuav38OGPVoeWymVP33YW4pVCqvEoBZ3MSneAcdd3HdgIS8u0JfoKKXypwmhlBt09SCSm1fiQA24/udv+XHb76xI0fLYSqmzaUIo5SqEVuCelsOY2M1CUj08sn+OvkRHKZUvTQhlwIAmA9h3VTV21THEbVpJym8HmL9JH+0opfLShFAGlA8pz/DoEUxMsPCeFp7+fSYvL9yGW8tjK6Vy0YRQRtza+FZONr6clIaGlhvXcXjfAWYk7Ql0WEqpIKIJoYwIdYTy1+i/MjHBQjJh7L5p/G9xspbHVkpl04RQhvT9U188V9ZhY1OLBps2I4d+54OVOwMdllIqSGhCKENCrBBGtRrFu3EGrwde3vspE5Zu1/LYSilAE0KZ8+cGf6ZcwytZ3dJJ1KZfuezYLt76TstjK6U0IZQ5DsvBfa3uY3IXXwXCF3dP5b3vd3DgpJbHVqqs04RQBvWo14OoBk1Z3iaEClv20vjEL1oeWymlCaEssozF6Faj+aiz4HXAMzumMW3VbnYcTgt0aEqpANKEUEZ1vaIr9epH83X7UEJTDhObupmXF2nhO6XKMk0IZZQxhjGtxzC1vRd3qPBIykzmbvidjXv01RVKlVWaEMqwDjU7cHWD9nzVORSz4wQ3pq3hxYVbAx2WUipANCGUYVlXCZ+19ZJZThi5eRbLkw/x/XYtj61UWaQJoYxrXb017RrE8VlcKN69p7g77Qde1PLYSpVJmhAUo1uPZk60l/QKwsANc1m/+zgLtDy2UmWOJgRFs8uaEd+wB1Piw/AczOTh1MW8tEjLYytV1mhCUADc1+o+vm7uJa0K9F67mJ0HTzJTy2MrVaZoQlAANKrSiN5X/pn340NwH/XwzMmv+N/iFNJdWh5bqbJCE4LKNjJ6JN83NZyIMrRPWsHR4yf54IedgQ5LKVVMCpUQjDGVjDHzjTGLjDFfGGNCjTGTjDErjTH/zDXdWW0qeNWvVJ8b/tSXd7o5cZ8Uxp/8ggnLfuHEaS2PrVRZUNgrhDuA8SLSC9gPDAAcItIJaGiMaWSMuenMtqIJWfnTvdH3suZPFodrWzT+aRXe1OO8/a2Wx1aqLChUQhCRCSLytT0YBdwJTLeHFwGxQEI+bSrI1apQi1sa38qEBAvPKXjt2Awmf7+Dg1oeW6lS75KeIRhjOgFVgN3AXrv5KFADiMin7cz5RxhjVhtjVh86dOhSQlFFaHjL4aQ0CGNffSeXr9pI5fSjvLpEy2MrVdoVOiEYY6oCrwH3AKlAOXtUBXu5+bXlISITRSRGRGKioqIKG4oqYtXLV2dAkwG8Hi94MwyvHprK1J92s1PLYytVqhX2oXIoMAN4TER+A5LIuSUUDew8R5sqIe5pcQ/76kaws0kolZJSaOA+yLivkwMdllLKjwp7hTAUaAM8YYxZBhhgkDFmPHAbMBeYlU+bKiGqhlflzqvv5NU4D14XvLxvCnPW72PTXi2PrVRpVdiHym+KSBURSbC7D/A9RE4EuonICRE5eWZbUQWtiseQZkM4Ubsi21qUI2ztLjrIbl5cqC/RUaq0KrIvponIMRGZLiL7z9emSo6KoRW5q9ldvNbFhdcL/9n5Kd8lH+KHX7Q8tlKlkX5TWZ3XnU3vxFWzKhvalIcNB7iW7YxdsE3LYytVCmlCUOdVPqQ8Q1sMZUKnTMSCv2/7lPW7j7Pw5wOBDk0pVcQ0IagL6t+kPyHVa/BThwq4tx7nTmsTLy3cquWxlSplNCGoCwp3hjOi5QgmtktHnHD3+un8ciiNz9fsvfDMSqkSQxOCKpCbGt1EZFRtvu0SSeYvp/ib9SP/XZys5bGVKkU0IagCCXGEcG/0vbzX5jTecOi7eha/nzjNRyt/C3RoSqkioglBFdgNV95AjagGLOwaScYuF0+bb3lj2XZOpmt5bKVKA00IqsCclpNRrUbxcctTuCMgduU8TqalM/HbXwMdmlKqCGhCUBeld/3e1ItqxJfdKpKx38v/WMikFVoeW6nSQBOCuiiWsRjdejQzrk4js5JF8xVLsNyneW3J9kCHppS6RJoQ1EW7ps41XFW9OTOuiSDjCLzu+pJPf9rFb0e0PLZSJZkmBHXRjDGMaT2GLxufIr2ag7rLf6CKOcW4RVoeW6mSTBOCKpTOtTrTumZbPuxenswTFhPSZvLl+n38vE+L2ipVUmlCUIVijGF069EsbnCKtMtDqLx8LX8K/YMXF2h5bKVKKk0IqtDa1WxHx1qdeLd7OO40i/8e+ZRvkw+x8pcjgQ5NKVUImhDUJRnTegzfX3GKE/XCCft+K23LHWXsgq1aHlupEkgTgrokLaNaklCnGxO6OfCkW7yw92PW7T7Oos1aHlupkkYTgrpk97W+j7WXZ3C4cQSycie9KvzOSwu34fHqVYJSJYkz0AGoku+qqlfRq14vXo1fxtPJFo9u/4hrav6D+z9dS8OoCCqGh1CxnNP+GZJnODLcidOhf5coFQw0IagicV+r++i3azH7WlSi9ur9PDhyP+/9EsL8Tb9zoQuFiFDHWYnCN+w8R7smFKX8QROCKhINKzfkLw3/wn9j5/HSJsOAHyfztw+/R0RIy/Rw8rSLk+kuTp525+p3cTLdnXdcuosDf6STcjA1expNKEoVD00IqsjcG30vfX6dx862l2FWH6HK7GcJubIZFZxhVHCEUsvhBEcoVAyFKiFghfiGHRH2zxC7C/V1lkMTilLFSBOCKjJ1IuvQr1E/xp/8nFfXGH555BNAMA4wlmAcgrEE64xh4wArq98eNg7BsgTjdGCcFqEhFlFOB9VDHFhOBybEmasLwQoNwYSGYEJDfV3lEExoGCYsDI8zlExHCJnGSYbXwWmvg3SPxSmPxSmPgzS3Ic1tSHVb/JFh+CPVcNJlOJhpSM6ATHHgwokLJ5k4cePAJTnDoaGhlAsvR/nwcCLLh2pCKSNEBEFyfmL/5SFkD2d9/Pq8/QihVijhzvCAbEdumhBUkRrRcgSzt8/mm793ZcCxOngz05GMjJwuMwPJzMSb6UIyM5FMF+Jy4c50IS433nQX4vIgbg/iciNuL+L25lqDAG67u0hG7EQDoZYQ5hCqOgRj5UpAuYazklZ2Assabw+LQxAHiCV4nb6fLqeFy7LIdFq4LAfpDgeZDot0y0GG08EBh8Uuh4MMy0mmWGQaBx7LgcfhwGs58DqciGUhTidYDnBY4HQgDgfG4Rs2dptxWBiHwWsMbmPwAB4ke+94EHtY8Iiv3yNe+4TkzXVSyurPdaISOU97zknQd/aTXCdAstt9s5xxwjxjfHZ/rmVnrceeO/tUm/Pdlpy5cpaRM0ayT8i5p84Zl/VblNN+Zj+55iCf9qJ3T6VmPHjjVD8tveD8nhCMMZOAq4G5IvKsv9enAqtmRE1ua3Ib7279lNjBI7ks/DLc4sbtdeMRDx6vB7e48Xg9eMRzVrvbm884twuPK9NOKJl4MzPxZmYgGZngyoQMF+LKhExXTudyYzLdGJfL7jwYlwfL5cbK89ODw+XBcntxuL04XF6cbsGZ6cXhFpwewenG1+YBp/fC+yBLiN3l8NqdG5cjA5cDXE5wOcDtgEwnuJ2Q6QC30+Q/3v7pcZzROQWvBV4neB0gjqxhX4KznL5E6HQImJwTmzEgJmvA7rDbjG9aI752sTtjz5drcox9pjWc0X7GNPm2n9HmW23OqdeQ/3KxpzPGYOyxxvjGZv/LGs4zjcmZxth9JmcNWdP6ftobe2a7sfIuK9e4nGHLt/+w4Ix5DSbPcptf3umcv0fFya8JwRhzE+AQkU7GmMnGmEYikuLPdarAG9piKJ+lfMag+YP8vi6nceK0nDhCHTjCHL5+48BhOXCY3MOhOdPa453GmT1d1rDTcp49b+5+DCFuCPNYOD1CiBtCshOGL4mEuMHh8uDwCE634HB5fYnH7fH1uz2YTA+hbg/hmW6M25egTKbbl8hcbkymC1wu3xVUmi8JSkYmZGaCyzfOnPfb4Geeboue13e2w4tBfGdexPj6JasfuzO+KxnA/ukbzhonZ/ZnD1t5xmF8bVnLyBqPyRpv2ckspz1nnG989rBlL8OysqfLPQ3GQH7jLF+/MRZi2UkgVzvGwlg5bcZelzFWTptlJwvL1+YJa+a343Qx/H2FkABMt/sXAbFAdkIwxowARgDUrVvXz6Go4lKtXDXe6fUOvx7/Nd8TcFZbgU7O+YzPareMZf9FVvaICLjdeDMyEVem7/Zb1hWU/VMyXXgz0kk/lU5a6mlOp50m43Q64hXE60W8Al7fbSHxevGKPWyPE6/XHo/d5vXdHvJ6EfGCV3L9zFkWWcv2zWhPL2CvD/HNZ7LmE6/vksXr9U0vvnmNN1e/eDF2HJaI72pDvPY67GXjmydrvUay1oc9v+RMn3sZ5B1v7A5y9Yud2s4x3lyoH7DOc8Mp5cQJ6NujGH5zzs/fCSEC2Gv3HwXa5B4pIhOBiQAxMTH6tdZSJDoqmuio6ECHUWoZYyAkBEdICL7/ZucWCUQVS1TqfLKSm3i9eD1evF4vHq8X8Xhp6AyOx7n+jiIVKGf3V0BLZSilyihj33YyloUVHOf/s/j7BJ2E7zYRQDSw08/rU0opVUj+zlOzgOXGmFrAdUBHP69PKaVUIfn1CkFETuJ7sJwIdBMRfb+iUkoFKb/fyRKRY+R80kgppVSQ0oe8SimlAE0ISimlbJoQlFJKAWCC5WXoxphDwG+FnL0acLgIwwkk3ZbgVFq2pbRsB+i2ZKknIkXy3cOgSQiXwhizWkRiAh1HUdBtCU6lZVtKy3aAbos/6C0jpZRSgCYEpZRSttKSECYGOoAipNsSnErLtpSW7QDdliJXKp4hKKWUunSl5QpBKaXUJSo1CcH43mlX0GlrGmO6+DMepcckGOkxCT7BdEwCUpXbGLMV2H9G81VALxHZYIxxAP8BxgHNAEtElhtj9olILWPM9cAhEfnJGPMC0AmoY4w5BTjwvX/pEJAiIsONMRWAh0XkP/a6BgPpwPf5xPY0sBToAfwBvAHMBP4sIp6i2wvBRY9JYBljqgJtgbUicthu02MSZEr7MfF7QjDG1ABmikhcrub9IpJwxnTvA2kAIuIxxiTh26D/B8wzxnQA0o0xlwP/BK63Z50AzADuBDYDtYBTwGLsBzUikmqMqWOMGSYi7wK328u6yV7GMRHpZ+/8k/gOUnWgJlAPSLNjsuzlXcSr1oOLMaYSMBXfL18a0F9EMtFjEjDGmCrAV8BcYLwx5hoROYQek4Czz18LRKS13VSqj4lfE4L9i/4BZ7/jr6IxZtkZbVfhy6wYY+oCiSIyyz6BvQLcAJTH98KdDwCXPd8ofG8JvBLf9lQHMuxxr+da/v3A28aYXcBy4N/4dv6fgEftaSoBlwGjgXXAD3b/n4wx39nT3gj8dHF7IqjcAYwXka+NMW8C1wJfosckkFoCD4lIov1/pg2wED0mweBlct76CKX8mPj7CsED9Admn9F+VETyvFHazrJZqgGvGWOeAvbgy4rjgLvs8Q/iy6J/AFmX2jXt+SLt9dbzLdb8LiJfi0gacKcxZizwFDAC36XfEeCTXPHWt9fVxF5mS+AJYDvwVxEp0b/kIjIh12AUcNDu12MSICLyLYAxpivQHnjaHqXHJICMMdfg+8s/9y2iUn1M/JoQ7Bfk+N4lmlf5C8y3xhgTDxh82bMlvkutLE0At90/Gt+lWSsRuc0Ycxe+dzkvAl4FwrNmMsa8C4wWkXRjTHV8l1tzci3XiW+HDwLG48vkz+M7aJnArwXa8BLAGNMJqCIiiXaTHpMAMr7/JP2BY+T8BanHJECMMaHAv4B++N78mKVUH5NAveq5rjFm8RltV2NfdtmigZuBD4H5InJX1ghjzCz7Zzd8O64WsPGMS7lXgXeBJfa03QHsHWqAzkBd4O1c89QDngUa2etvBTQAPrfHzy/MxgYb43uA+Rq+/ZtFj0kAie8LQfcZY54B+gDT0GMSSI8CE0Tk+Bl/0JbuYyIifu+AZbn6GwBz8pnmfaB+ruGngVvx3Z/bDyzL1R3Gd3kUii+LLrPnGYrvXizAT7mWFYHvNZ5R9vBj+O7LPQA8dUYcI/Hd5xsAjLXbnsJ3X84qjv3l52MRCnwD9NRjEhwd8Agw2O5/DeitxyTgx+S7XPvxOL4TdKk/JsW1c5fl6n8BuCufaT7GV8YVe0f9au+MpsD7Z0w7+4wDMBrfA5uNQAy+bPgOEGqPvxX4G3C5vcNeyzXv68AXQEN72OD7aNcmfJ/6aIDvo1s/AjGB/kUtgmMxEt9tiaxf0P56TAJ+TKoAX+M7CU2wt1ePSZB05Jy0S/0xKe4d2wBIAkJytYUBq+0uzG7rAnxk97fIvVOBgcAGfPfyDDAPWGPvrCb2NFHAi8DkM9b/F2BAPnENxHeZVQXfRzKfByrge8C3zD5Qdewde2Wgf0H1mOgx0WOix8Qfx6TYaxkZY0JExHVGmyUF/MyyMcYhub5kYYwJFd/n6IsqPqeIuO1+g+9Sy5M1LMW9w4qBHpPgo8ck+JSFY6LF7ZRSSgGlqJaRUkqpS6MJQSmlFKAJQSmllE0TglJKKUATglJKKdv/B+0eMQO2/Yl8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_xiaofei.iloc[0,:-1].plot(label='顾客编号52')\n",
    "data_xiaofei.iloc[1,:-1].plot(label='顾客编号210')\n",
    "data_xiaofei.iloc[2,:-1].plot(label='顾客编号51')\n",
    "data_xiaofei.iloc[3,:-1].plot(label='顾客编号47')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>销售日期</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.40000</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8.30000</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>11.90000</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>8.90000</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42811</th>\n",
       "      <td>1605</td>\n",
       "      <td>3.90600</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42812</th>\n",
       "      <td>1572</td>\n",
       "      <td>0.86240</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42813</th>\n",
       "      <td>1170</td>\n",
       "      <td>14.50000</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42814</th>\n",
       "      <td>2605</td>\n",
       "      <td>1.83808</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42815</th>\n",
       "      <td>2610</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>2015-04-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42713 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       顾客编号      销售金额       销售日期\n",
       "1         1   3.00000 2015-01-01\n",
       "2         2   2.40000 2015-01-01\n",
       "3         3   8.30000 2015-01-01\n",
       "4         4  11.90000 2015-01-01\n",
       "5         5   8.90000 2015-01-01\n",
       "...     ...       ...        ...\n",
       "42811  1605   3.90600 2015-04-30\n",
       "42812  1572   0.86240 2015-04-30\n",
       "42813  1170  14.50000 2015-04-30\n",
       "42814  2605   1.83808 2015-04-30\n",
       "42815  2610   8.00000 2015-04-30\n",
       "\n",
       "[42713 rows x 3 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 实验11 统计分析每位顾客每月的消费额\n",
    "data_money=new[['顾客编号','销售金额','销售日期']]\n",
    "data_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1、 拆分出四个月的表\n",
    "# 拆分出四个月的表\n",
    "data_money1 = data_money.loc[data_month['月份'] == 1,:]\n",
    "data_money2 = data_money.loc[data_month['月份'] == 2,:]\n",
    "data_money3 = data_money.loc[data_month['月份'] == 3,:]\n",
    "data_money4 = data_money.loc[data_month['月份'] == 4,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>销售日期</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  销售金额       销售日期\n",
       "1     1   3.0 2015-01-01\n",
       "2     2   2.4 2015-01-01\n",
       "3     3   8.3 2015-01-01\n",
       "4     4  11.9 2015-01-01\n",
       "5     5   8.9 2015-01-01"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_money1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2、对利用groupby对顾客编号的每月的销售金额进行分类汇总\n",
    "data_money1_time=data_money1.groupby(by='顾客编号').sum()\n",
    "data_money2_time=data_money2.groupby(by='顾客编号').sum()\n",
    "data_money3_time=data_money3.groupby(by='顾客编号').sum()\n",
    "data_money4_time=data_money4.groupby(by='顾客编号').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>顾客编号</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.04988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.29728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.80072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.97024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.90264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>100.10000</td>\n",
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       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>9.20792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>21.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>26.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1224</th>\n",
       "      <td>28.80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1225 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           销售金额\n",
       "顾客编号           \n",
       "0       7.04988\n",
       "1      12.29728\n",
       "2      53.80072\n",
       "3      60.97024\n",
       "4      27.90264\n",
       "...         ...\n",
       "1220  100.10000\n",
       "1221    9.20792\n",
       "1222   21.50000\n",
       "1223   26.90000\n",
       "1224   28.80000\n",
       "\n",
       "[1225 rows x 1 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_money1_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3、将四个销售金额表进行汇总"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_money1_time['二月消费额']=data_money2_time\n",
    "data_money1_time['三月消费额']=data_money3_time\n",
    "data_money1_time['四月消费额']=data_money4_time\n",
    "data_money1_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>三月消费额</th>\n",
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       "      <td>77.12782</td>\n",
       "      <td>51.41672</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>100.10000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1221</th>\n",
       "      <td>9.20792</td>\n",
       "      <td>20.90280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1222</th>\n",
       "      <td>21.50000</td>\n",
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       "      <td>18.70532</td>\n",
       "      <td>NaN</td>\n",
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       "<p>1225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           销售金额      二月消费额     三月消费额      四月消费额\n",
       "顾客编号                                           \n",
       "0       7.04988        NaN       NaN   13.59540\n",
       "1      12.29728   36.33560       NaN        NaN\n",
       "2      53.80072   38.78324       NaN        NaN\n",
       "3      60.97024  381.18932  77.12782   51.41672\n",
       "4      27.90264        NaN       NaN  155.92760\n",
       "...         ...        ...       ...        ...\n",
       "1220  100.10000        NaN       NaN        NaN\n",
       "1221    9.20792   20.90280       NaN        NaN\n",
       "1222   21.50000        NaN       NaN        NaN\n",
       "1223   26.90000        NaN       NaN   14.07308\n",
       "1224   28.80000        NaN  18.70532        NaN\n",
       "\n",
       "[1225 rows x 4 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_money1_time.rename(columns={'销售金额':'一月销售额'}, inplace=True)      #修改列标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>一月销售额</th>\n",
       "      <th>二月消费额</th>\n",
       "      <th>三月消费额</th>\n",
       "      <th>四月消费额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>顾客编号</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>77.12782</td>\n",
       "      <td>51.41672</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.90264</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         一月销售额      二月消费额     三月消费额      四月消费额\n",
       "顾客编号                                          \n",
       "0      7.04988        NaN       NaN   13.59540\n",
       "1     12.29728   36.33560       NaN        NaN\n",
       "2     53.80072   38.78324       NaN        NaN\n",
       "3     60.97024  381.18932  77.12782   51.41672\n",
       "4     27.90264        NaN       NaN  155.92760"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_money1_time.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对空值进行处理\n",
    "data_m_t=data_money1_time.fillna(value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实验12 统计生鲜类产品的每周销售金额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>洗化</td>\n",
       "      <td>3018</td>\n",
       "      <td>卫生巾</td>\n",
       "      <td>301802</td>\n",
       "      <td>夜用卫生巾</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-3018020109</td>\n",
       "      <td>10片</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>包</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称       销售日期    销售月份  \\\n",
       "1     1    20   粮油  2014   酱菜类  201401      榨菜 2015-01-01  201501   \n",
       "2     2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳 2015-01-01  201501   \n",
       "3     3    15   日配  1503  冷藏料理  150305   冷藏面食类 2015-01-01  201501   \n",
       "4     4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳 2015-01-01  201501   \n",
       "5     5    30   洗化  3018   卫生巾  301802   夜用卫生巾 2015-01-01  201501   \n",
       "\n",
       "            商品编码    规格型号  商品类型 单位  销售数量  销售金额  商品单价 是否促销  \n",
       "1  DW-2014010019     60g  一般商品  袋   6.0   3.0   0.5    否  \n",
       "2  DW-1505020011    150g  一般商品  袋   1.0   2.4   2.4    否  \n",
       "3  DW-1503050035    500g  一般商品  袋   1.0   8.3   8.3    否  \n",
       "4  DW-1505020020  100g*8  一般商品  袋   1.0  11.9  11.9    否  \n",
       "5  DW-3018020109     10片  一般商品  包   1.0   8.9   8.9    否  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 42713 entries, 1 to 42815\n",
      "Data columns (total 17 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   顾客编号    42713 non-null  int64         \n",
      " 1   大类编码    42713 non-null  int64         \n",
      " 2   大类名称    42713 non-null  object        \n",
      " 3   中类编码    42713 non-null  int64         \n",
      " 4   中类名称    42713 non-null  object        \n",
      " 5   小类编码    42713 non-null  int64         \n",
      " 6   小类名称    42713 non-null  object        \n",
      " 7   销售日期    42713 non-null  datetime64[ns]\n",
      " 8   销售月份    42713 non-null  int64         \n",
      " 9   商品编码    42713 non-null  object        \n",
      " 10  规格型号    42713 non-null  object        \n",
      " 11  商品类型    42713 non-null  object        \n",
      " 12  单位      42713 non-null  object        \n",
      " 13  销售数量    42713 non-null  float64       \n",
      " 14  销售金额    42713 non-null  float64       \n",
      " 15  商品单价    42713 non-null  float64       \n",
      " 16  是否促销    42713 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(3), int64(5), object(8)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>商品类型</th>\n",
       "      <th>销售日期</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>一般商品</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>一般商品</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>一般商品</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>一般商品</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>一般商品</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   商品类型       销售日期  销售金额\n",
       "1  一般商品 2015-01-01   3.0\n",
       "2  一般商品 2015-01-01   2.4\n",
       "3  一般商品 2015-01-01   8.3\n",
       "4  一般商品 2015-01-01  11.9\n",
       "5  一般商品 2015-01-01   8.9"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sh=new[['商品类型','销售日期','销售金额']]\n",
    "data_sh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>商品类型</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>生鲜</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>5.39840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>生鲜</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>生鲜</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1.25440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>生鲜</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>3.84960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>生鲜</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>12.54400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42806</th>\n",
       "      <td>生鲜</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>4.62480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42808</th>\n",
       "      <td>生鲜</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>7.67280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42811</th>\n",
       "      <td>生鲜</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>3.90600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42812</th>\n",
       "      <td>生鲜</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>0.86240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42814</th>\n",
       "      <td>生鲜</td>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>1.83808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16108 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      商品类型       销售日期      销售金额\n",
       "6       生鲜 2015-01-01   5.39840\n",
       "8       生鲜 2015-01-01   2.00000\n",
       "11      生鲜 2015-01-01   1.25440\n",
       "12      生鲜 2015-01-01   3.84960\n",
       "16      生鲜 2015-01-01  12.54400\n",
       "...    ...        ...       ...\n",
       "42806   生鲜 2015-04-30   4.62480\n",
       "42808   生鲜 2015-04-30   7.67280\n",
       "42811   生鲜 2015-04-30   3.90600\n",
       "42812   生鲜 2015-04-30   0.86240\n",
       "42814   生鲜 2015-04-30   1.83808\n",
       "\n",
       "[16108 rows x 3 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sx=data_sh[data_sh['商品类型']=='生鲜']\n",
    "data_sx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售金额</th>\n",
       "      <th>天数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>销售日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>659.34862</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>886.91812</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>654.65834</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>538.31654</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>824.41628</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 销售金额  天数\n",
       "销售日期                     \n",
       "2015-01-01  659.34862   1\n",
       "2015-01-02  886.91812   2\n",
       "2015-01-03  654.65834   3\n",
       "2015-01-04  538.31654   4\n",
       "2015-01-05  824.41628   5"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sx=data_sx.groupby('销售日期').sum()[['销售金额']]\n",
    "data_sx['天数']=range(1,len(data_sx)+1)\n",
    "data_sx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weeks(DataFrame):\n",
    "    import math\n",
    "    x = DataFrame['天数'] - DataFrame['天数'].min() + 1\n",
    "    week = []\n",
    "    for i in x:\n",
    "        week.append(math.ceil(i / 7))\n",
    "    return week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售金额</th>\n",
       "      <th>周数</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>销售日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2015-01-02</th>\n",
       "      <td>886.91812</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>654.65834</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>538.31654</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>824.41628</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 销售金额  周数\n",
       "销售日期                     \n",
       "2015-01-01  659.34862   1\n",
       "2015-01-02  886.91812   1\n",
       "2015-01-03  654.65834   1\n",
       "2015-01-04  538.31654   1\n",
       "2015-01-05  824.41628   1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sx['周数']=weeks(data_sx)\n",
    "data_sx.drop(['天数'],axis=1,inplace=True)\n",
    "data_sx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售金额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>周数</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>5980.33440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5977.45316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8224.85114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9966.71172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4603.78258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          销售金额\n",
       "周数            \n",
       "1   5980.33440\n",
       "2   5977.45316\n",
       "3   8224.85114\n",
       "4   9966.71172\n",
       "5   4603.78258"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sx=data_sx.groupby('周数').sum()[['销售金额']]\n",
    "data_sx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-75-b9f31f525245>:2: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n",
      "  plt.pie(x=data_sx.values,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制饼图，textprops={'fontproperties':font}显示中文\n",
    "plt.pie(x=data_sx.values,\n",
    "        labels=data_sx.index,\n",
    "        autopct='%.1f%%',\n",
    "        shadow=False,\n",
    "        startangle=90,\n",
    "        center = (3,3),)\n",
    "\n",
    "# 添加标题，fontproperties=font显示中文\n",
    "plt.title(\"生鲜类产品周销售金额统计\")\n",
    "# 饼图保持圆形\n",
    "plt.axis('equal')\n",
    "# 显示图像\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_axis_data = data_sx.index\n",
    "y_axis_data = data_sx.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot中参数的含义分别是横轴值，纵轴值，线的形状，颜色，透明度,线的宽度和标签\n",
    "plt.plot(x_axis_data, y_axis_data, 'ro-', color='#4169E1', alpha=0.8, linewidth=1, label='生鲜类产品')\n",
    "\n",
    "# 显示标签，如果不加这句，即使在plot中加了label='一些数字'的参数，最终还是不会显示标签\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.xlabel('周数')\n",
    "plt.ylabel('销售金额')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实验13   统计分析一般类产品的每周销售金额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顾客编号</th>\n",
       "      <th>大类编码</th>\n",
       "      <th>大类名称</th>\n",
       "      <th>中类编码</th>\n",
       "      <th>中类名称</th>\n",
       "      <th>小类编码</th>\n",
       "      <th>小类名称</th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售月份</th>\n",
       "      <th>商品编码</th>\n",
       "      <th>规格型号</th>\n",
       "      <th>商品类型</th>\n",
       "      <th>单位</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>销售金额</th>\n",
       "      <th>商品单价</th>\n",
       "      <th>是否促销</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>粮油</td>\n",
       "      <td>2014</td>\n",
       "      <td>酱菜类</td>\n",
       "      <td>201401</td>\n",
       "      <td>榨菜</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-2014010019</td>\n",
       "      <td>60g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020011</td>\n",
       "      <td>150g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1503</td>\n",
       "      <td>冷藏料理</td>\n",
       "      <td>150305</td>\n",
       "      <td>冷藏面食类</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1503050035</td>\n",
       "      <td>500g</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>日配</td>\n",
       "      <td>1505</td>\n",
       "      <td>冷藏乳品</td>\n",
       "      <td>150502</td>\n",
       "      <td>冷藏加味酸乳</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-1505020020</td>\n",
       "      <td>100g*8</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>袋</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>11.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>洗化</td>\n",
       "      <td>3018</td>\n",
       "      <td>卫生巾</td>\n",
       "      <td>301802</td>\n",
       "      <td>夜用卫生巾</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>201501</td>\n",
       "      <td>DW-3018020109</td>\n",
       "      <td>10片</td>\n",
       "      <td>一般商品</td>\n",
       "      <td>包</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   顾客编号  大类编码 大类名称  中类编码  中类名称    小类编码    小类名称       销售日期    销售月份  \\\n",
       "1     1    20   粮油  2014   酱菜类  201401      榨菜 2015-01-01  201501   \n",
       "2     2    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳 2015-01-01  201501   \n",
       "3     3    15   日配  1503  冷藏料理  150305   冷藏面食类 2015-01-01  201501   \n",
       "4     4    15   日配  1505  冷藏乳品  150502  冷藏加味酸乳 2015-01-01  201501   \n",
       "5     5    30   洗化  3018   卫生巾  301802   夜用卫生巾 2015-01-01  201501   \n",
       "\n",
       "            商品编码    规格型号  商品类型 单位  销售数量  销售金额  商品单价 是否促销  \n",
       "1  DW-2014010019     60g  一般商品  袋   6.0   3.0   0.5    否  \n",
       "2  DW-1505020011    150g  一般商品  袋   1.0   2.4   2.4    否  \n",
       "3  DW-1503050035    500g  一般商品  袋   1.0   8.3   8.3    否  \n",
       "4  DW-1505020020  100g*8  一般商品  袋   1.0  11.9  11.9    否  \n",
       "5  DW-3018020109     10片  一般商品  包   1.0   8.9   8.9    否  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   商品类型       销售日期  销售金额\n",
       "1  一般商品 2015-01-01   3.0\n",
       "2  一般商品 2015-01-01   2.4\n",
       "3  一般商品 2015-01-01   8.3\n",
       "4  一般商品 2015-01-01  11.9\n",
       "5  一般商品 2015-01-01   8.9"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_yb=new[['商品类型','销售日期','销售金额']]\n",
    "data_yb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
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       "      <th>42810</th>\n",
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      ],
      "text/plain": [
       "       商品类型       销售日期  销售金额\n",
       "1      一般商品 2015-01-01   3.0\n",
       "2      一般商品 2015-01-01   2.4\n",
       "3      一般商品 2015-01-01   8.3\n",
       "4      一般商品 2015-01-01  11.9\n",
       "5      一般商品 2015-01-01   8.9\n",
       "...     ...        ...   ...\n",
       "42805  一般商品 2015-04-30   4.3\n",
       "42807  一般商品 2015-04-30  14.9\n",
       "42809  一般商品 2015-04-30   6.5\n",
       "42810  一般商品 2015-04-30   4.7\n",
       "42813  一般商品 2015-04-30  14.5\n",
       "\n",
       "[26056 rows x 3 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_yb=data_sh[data_sh['商品类型']=='一般商品']\n",
    "data_yb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2015-01-02</th>\n",
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       "      <td>2078.12950</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>2457.83654</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  销售金额  天数\n",
       "销售日期                      \n",
       "2015-01-01  2336.76612   1\n",
       "2015-01-02  2820.23820   2\n",
       "2015-01-03  1784.00780   3\n",
       "2015-01-04  2078.12950   4\n",
       "2015-01-05  2457.83654   5"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_yb=data_yb.groupby('销售日期').sum()[['销售金额']]\n",
    "data_yb['天数']=range(1,len(data_yb)+1)\n",
    "data_yb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>2015-01-04</th>\n",
       "      <td>2078.12950</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>2457.83654</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  销售金额  周数\n",
       "销售日期                      \n",
       "2015-01-01  2336.76612   1\n",
       "2015-01-02  2820.23820   1\n",
       "2015-01-03  1784.00780   1\n",
       "2015-01-04  2078.12950   1\n",
       "2015-01-05  2457.83654   1"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_yb['周数']=weeks(data_yb)\n",
    "data_yb.drop(['天数'],axis=1,inplace=True)\n",
    "data_yb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>18778.49774</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19922.41504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20216.45682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22615.15290</td>\n",
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       "      <th>5</th>\n",
       "      <td>31453.76678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           销售金额\n",
       "周数             \n",
       "1   18778.49774\n",
       "2   19922.41504\n",
       "3   20216.45682\n",
       "4   22615.15290\n",
       "5   31453.76678"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_yb=data_yb.groupby('周数').sum()[['销售金额']]\n",
    "data_yb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-85-0f4105758126>:2: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n",
      "  plt.pie(x=data_yb.values,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制饼图，textprops={'fontproperties':font}显示中文\n",
    "plt.pie(x=data_yb.values,\n",
    "        labels=data_yb.index,\n",
    "        autopct='%.1f%%',\n",
    "        shadow=False,\n",
    "        startangle=90,\n",
    "        center = (3,3),)\n",
    "\n",
    "# 添加标题，fontproperties=font显示中文\n",
    "plt.title(\"一般类产品周销售金额统计\")\n",
    "# 饼图保持圆形\n",
    "plt.axis('equal')\n",
    "# 显示图像\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_axis_data = data_yb.index\n",
    "y_axis_data = data_yb.values\n",
    "# plot中参数的含义分别是横轴值，纵轴值，线的形状，颜色，透明度,线的宽度和标签\n",
    "plt.plot(x_axis_data, y_axis_data, 'ro-', color='red', alpha=0.8, linewidth=1, label='一般类产品')\n",
    "# 显示标签，如果不加这句，即使在plot中加了label='一些数字'的参数，最终还是不会显示标签\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.xlabel('周数')\n",
    "plt.ylabel('销售金额')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
